3_LATE_FUSION_SEGNET_2020_CLOUD.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"5RTOqgfFS4Dg"},"source":["# SEMANTIC SEGMENTATION USING FEATURE LEVEL FUSION FOR THE YEAR 2019"]},{"cell_type":"markdown","metadata":{"id":"2NkMszpE6JwW"},"source":["# 1. VISUALIZE THE SATELLITE DATA ALONG WITH RASTERIZED GROUND TRUTH "]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24142,"status":"ok","timestamp":1654293010178,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"RCWEnojSPJrS","outputId":"4cfbaedb-6fb2-482a-f319-f232b03325e8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["# MOUNT GOOGLE DRIVE\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6848,"status":"ok","timestamp":1654293020231,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"GS5LVjr_PWS2","outputId":"7c99de38-1209-4d2c-a3df-dae65f850c0d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Populating the interactive namespace from numpy and matplotlib\n"]}],"source":["# IMPORT ESSENTIAL LIBRARIES\n","import os\n","import numpy as np\n","import gdal\n","import keras\n","import tensorflow as tf\n","%matplotlib inline\n","%pylab inline\n","pylab.rcParams['figure.figsize'] = (10, 10)\n","import matplotlib.pyplot as plt\n","import matplotlib.patches as mpatches\n","from skimage import exposure\n","from google.colab import drive"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":40,"status":"ok","timestamp":1654293020234,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"0GUtEnGWQoE-"},"outputs":[],"source":["# DEFINING A FUNCTION WHICH TAKES A \"file_path\" AND RETURNS A \"tif\" FILE AS A 3-DIMENSIONAL NUMPY ARRAY AS: WIDTH X HEIGHT X BANDS\n","def gtiff_to_array(file_path):\n","    data = gdal.Open(file_path)\n","    bands = [data.GetRasterBand(i+1).ReadAsArray() for i in range(data.RasterCount)]\n","    return np.stack(bands, axis = 2)"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":38,"status":"ok","timestamp":1654293020237,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"93CbupX3zeqP"},"outputs":[],"source":["# DEFINING A FUNCTION TO FETCH THE IMAGE ARRAY INFORMATION FOR VISUALIZATION\n","def fetch_tiles(path):\n","    tiles = {}\n","    for f in path:\n","      _, ext = os.path.splitext(f)\n","      if ext.lower() == \".tif\":\n","        imgtype = f[-7:-4]\n","        tiles[imgtype] = gtiff_to_array(f)\n","    return tiles"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":38,"status":"ok","timestamp":1654293020241,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"AJzSyOmp33KG"},"outputs":[],"source":["# DEFINING A FUNCTION TO VISUALIZE THE LABELS PREPARED FROM THE REFERENCE IMAGES\n","def visualize_labels(labels, fig_width = 5, fig_height = 5):\n","    fig = plt.figure(figsize = (fig_width, fig_height))\n","    a = fig.add_subplot(1, 1, 1)\n","    values = np.unique(labels.ravel())\n","    im = plt.imshow(labels[:, :, 0])\n","    a.set_title(\"GROUND TRUTH\")\n","    # get the colors of the values, according to the \n","    # colormap used by imshow\n","    colors = [im.cmap(im.norm(value)) for value in values]\n","    # create a patch (proxy artist) for every color \n","    labels = [\"DEFORESTATION\", \"NON-DEFORESTATION\"]\n","    patches = [mpatches.Patch(color = colors[i], label = j) for i, j in zip(range(len(values)), labels)]\n","    # put those patched as legend-handles into the legend\n","    plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":36,"status":"ok","timestamp":1654293020242,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"sGjgT1Vt5CRt"},"outputs":[],"source":["# DEFINING A FUNCTION TO VISUALIZE THE SATELLITE IMAGE DATA\n","def visualize_data(data, title, fig_width = 5, fig_height = 5):\n","    # Visualize only RGB Bands\n","    data = data[:, :, 0:-1]\n","    # data = data[:, :, 0]\n","    _ = data[:, :, 0].copy()\n","    data[:, :, 0] = data[:, :, 2]\n","    data[:, :, 2] = _\n","    data = data.astype(np.float)\n","    \n","    # Perform Stretching for Better Visualization\n","    for i in range(data.shape[2]):\n","        p2, p98 = np.percentile(data[:, :, i], (2, 98))\n","        data[:, :, i] = exposure.rescale_intensity(data[:, :, i],\n","                                                      in_range=(p2, p98))\n","    fig = plt.figure(figsize = (fig_width, fig_height))\n","    a = fig.add_subplot(1,1,1)\n","    a.set_title(title)\n","    plt.imshow(data)"]},{"cell_type":"markdown","metadata":{"id":"FkiKZ-9i9Hhz"},"source":["# OPENING SENTINEL-1 AND SENTINEL-2 FULL IMAGE TO TEST THE MODEL "]},{"cell_type":"code","execution_count":7,"metadata":{"executionInfo":{"elapsed":1771,"status":"ok","timestamp":1654293021980,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"rVxddJAW_Al3"},"outputs":[],"source":["# PUTTING ALL THE \"IMAGES\" AND \"LABELS\" INSIDE VARIABLES \"image_path\" AND \"mask_path\"\n","image_path_full_S1 = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_FULL/\"\n","image_path_full_S2 = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_FULL/\"\n","mask_path_full = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_FULL/\"\n","\n","# GETTING THE NAMES OF IMAGES AND MASKS\n","image_list_full_S1 = os.listdir(image_path_full_S1)\n","image_list_full_S2 = os.listdir(image_path_full_S2)\n","mask_list_full = os.listdir(mask_path_full)\n","\n","# SORTING THE NAMES OF IMAGES AND MASKS IN ASCENDING NUMBER ORDER \n","image_list_full_S1.sort()\n","image_list_full_S2.sort()\n","mask_list_full.sort()\n","\n","# CONCATENATING THE DIRECTORY WITH THE SPECIFIC IMAGE NAME AND EXTENSION\n","image_list_full_S1 = [image_path_full_S1 + i for i in image_list_full_S1]\n","image_list_full_S2 = [image_path_full_S2 + i for i in image_list_full_S2]\n","mask_list_full = [mask_path_full + i for i in mask_list_full]"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1654293021982,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"VBELpDmu_SUc","outputId":"38be4384-4c8c-4a35-d57a-6832915375d6"},"outputs":[{"output_type":"stream","name":"stdout","text":["SENTINEL-1 IMAGES: ['/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_FULL/raster.tif']\n","SENTINEL-2 IMAGES: ['/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_FULL/raster.tif']\n","LABELS: ['/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_FULL/raster.tif']\n"]}],"source":["# PRINTING DIRECTORY OF ALL THE \"IMAGES\" AND \"LABELS\"\n","print(\"SENTINEL-1 IMAGES:\", image_list_full_S1)\n","print(\"SENTINEL-2 IMAGES:\", image_list_full_S2)\n","print(\"LABELS:\", mask_list_full)"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":25539,"status":"ok","timestamp":1654293047511,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"9-Tsdi16CAfm"},"outputs":[],"source":["# FETCH ALL THE ARRAY INFORMATION WITHIN THE VARIABLE \"image_tiles\" AND \"label_tiles\" FOR VISUALIZATION\n","image_tiles_full_S1 = fetch_tiles(image_list_full_S1)\n","image_tiles_full_S2 = fetch_tiles(image_list_full_S2)\n","label_tiles_full = fetch_tiles(mask_list_full)"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"elapsed":6792,"status":"ok","timestamp":1654293054278,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"pH5CqUwG6-dU","outputId":"9da40b85-ebec-4ddc-8768-91b2658cd0c2"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 360x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# IF YOU WANT TO VISUALIZE AN IMAGE AND LABEL BETWEEN 0 TO 9, RIGHT AN UNDERSCORE BEFORE IT E.G. \"R_0\" INSTEAD OF 0 OR \"R_9\" INSTEAD OF 9\n","# IF YOU WANT TO VISUALIZE AN IMAGE AND LABEL BETWEEN 10 TO 99, RIGHT AN UNDERSCORE BEFORE IT E.G. \"_10\" INSTEAD OF 10 OR \"_90\" INSTEAD OF 90\n","# IF YOU WANT TO VISUALIZE AN IMAGE AND LABEL BETWEEN 100 TO 143, RIGHT IT AS IT IS I.E. \"100\" FOR 1O0 OR \"119\" FOR \"119\"\n","image_number_full = \"ter\"\n","# visualize_data(image_tiles_full_S1[image_number_full], \"INPUT IMAGE\")\n","# visualize_data(image_tiles_full_S2[image_number_full], \"INPUT IMAGE\")\n","visualize_labels(label_tiles_full[image_number_full])"]},{"cell_type":"markdown","metadata":{"id":"6KInyDZlFX4I"},"source":["# OPENING SENTINEL-1 AND SENTINEL-2 16 GRID IMAGE TO TEST THE MODEL "]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":353,"status":"ok","timestamp":1654293054609,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"hQ6vsrtZFbMo"},"outputs":[],"source":["# PUTTING ALL THE \"IMAGES\" AND \"LABELS\" INSIDE VARIABLES \"image_path\" AND \"mask_path\"\n","image_path_S1_16_grid = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/\"\n","image_path_S2_16_grid = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/\"\n","mask_path_16_grid = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/\"\n","\n","# GETTING THE NAMES OF IMAGES AND MASKS\n","image_list_S1_16_grid = os.listdir(image_path_S1_16_grid)\n","image_list_S2_16_grid = os.listdir(image_path_S2_16_grid)\n","mask_list_16_grid = os.listdir(mask_path_16_grid)\n","\n","# SORTING THE NAMES OF IMAGES AND MASKS IN ASCENDING NUMBER ORDER \n","image_list_S1_16_grid.sort()\n","image_list_S2_16_grid.sort()\n","mask_list_16_grid.sort()\n","\n","# CONCATENATING THE DIRECTORY WITH THE SPECIFIC IMAGE NAME AND EXTENSION\n","image_list_S1_16_grid = [image_path_S1_16_grid + i for i in image_list_S1_16_grid]\n","image_list_S2_16_grid = [image_path_S2_16_grid + i for i in image_list_S2_16_grid]\n","mask_list_16_grid = [mask_path_16_grid + i for i in mask_list_16_grid]"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":41,"status":"ok","timestamp":1654293054612,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"oqUMo9KqFbHX","outputId":"98073761-a673-418c-e2a6-f022d5280bae"},"outputs":[{"output_type":"stream","name":"stdout","text":["SENTINEL-1 IMAGES: ['/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_0.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_1.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_10.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_11.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_12.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_13.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_14.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_15.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_2.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_3.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_4.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_5.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_6.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_7.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_8.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL1/2020/20201026/IMAGE_16_GRID/RASTER_9.tif']\n","SENTINEL-2 IMAGES: ['/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_0.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_1.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_10.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_11.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_12.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_13.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_14.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_15.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_2.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_3.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_4.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_5.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_6.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_7.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_8.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_9.tif']\n","LABELS: ['/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_0.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_1.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_10.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_11.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_12.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_13.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_14.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_15.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_2.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_3.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_4.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_5.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_6.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_7.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_8.tif', '/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/MASK_2020/MASK_16_GRID/RASTER_9.tif']\n"]}],"source":["# PRINTING DIRECTORY OF ALL THE \"IMAGES\" AND \"LABELS\"\n","print(\"SENTINEL-1 IMAGES:\", image_list_S1_16_grid)\n","print(\"SENTINEL-2 IMAGES:\", image_list_S2_16_grid)\n","print(\"LABELS:\", mask_list_16_grid)"]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":44544,"status":"ok","timestamp":1654293099137,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"mKf-38dxGprZ"},"outputs":[],"source":["# FETCH ALL THE ARRAY INFORMATION WITHIN THE VARIABLE \"image_tiles\" AND \"label_tiles\" FOR VISUALIZATION\n","image_tiles_S1_16_grid = fetch_tiles(image_list_S1_16_grid)\n","image_tiles_S2_16_grid = fetch_tiles(image_list_S2_16_grid)\n","label_tiles_16_grid = fetch_tiles(mask_list_16_grid)"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"elapsed":1279,"status":"ok","timestamp":1654293100398,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"oZyRqBl6FbCM","outputId":"170e6521-c9d5-4cab-89b8-bbde356bfcaf"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 360x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# IF YOU WANT TO VISUALIZE AN IMAGE AND LABEL BETWEEN 0 TO 9, RIGHT AN UNDERSCORE BEFORE IT E.G. \"R_0\" INSTEAD OF 0 OR \"R_9\" INSTEAD OF 9\n","# IF YOU WANT TO VISUALIZE AN IMAGE AND LABEL BETWEEN 10 TO 99, RIGHT AN UNDERSCORE BEFORE IT E.G. \"_10\" INSTEAD OF 10 OR \"_90\" INSTEAD OF 90\n","# IF YOU WANT TO VISUALIZE AN IMAGE AND LABEL BETWEEN 100 TO 143, RIGHT IT AS IT IS I.E. \"100\" FOR 1O0 OR \"119\" FOR \"119\"\n","image_number_16_grid = \"R_0\"\n","# visualize_data(image_tiles_S1_16_grid[image_number_16_grid], \"INPUT IMAGE\")\n","# visualize_data(image_tiles_S2_16_grid[image_number_16_grid], \"INPUT IMAGE\")\n","visualize_labels(label_tiles_16_grid[image_number_16_grid])"]},{"cell_type":"markdown","metadata":{"id":"JZ6mTWXQ6VQN"},"source":["# 3. SPLITTING THE TILES IN THE FORM OF PATCHES IN A GRID WISE MANNER "]},{"cell_type":"markdown","metadata":{"id":"26EZk9NiHGZX"},"source":["# FULL IMAGE"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":49,"status":"ok","timestamp":1654293100405,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"IWMnc-SbKN3E","outputId":"a275109e-f248-4826-ffef-e42985daaad9"},"outputs":[{"output_type":"stream","name":"stdout","text":["NUMBER OF BANDS IN SENTINEL-1 RASTER: 2\n","NUMBER OF BANDS IN SENTINEL-2 RASTER: 4\n"]}],"source":["# SETTING UP THE PATCH SIZE AND IDENTIFICATION OF THE NUMBER OF BANDS\n","patch_size = 256\n","nband_S1 = image_tiles_full_S1[\"ter\"].shape[-1]\n","nband_S2 = image_tiles_full_S2[\"ter\"].shape[-1]\n","print(\"NUMBER OF BANDS IN SENTINEL-1 RASTER:\", nband_S1)\n","print(\"NUMBER OF BANDS IN SENTINEL-2 RASTER:\", nband_S2)"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":27,"status":"ok","timestamp":1654293100408,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"hyFwXnBRKN0s"},"outputs":[],"source":["# DEFINING A FUNCTION FOR CONVERTING IMAGES AND LABELS INTO PATCHES OF 256 x 256\n","def gridwise_sample(imgarray, patchsize):\n","    nrows, ncols, nbands = imgarray.shape\n","    patchsamples = np.zeros(shape = (0, patchsize, patchsize, nbands),\n","                            dtype = imgarray.dtype)\n","    for i in range(int(nrows/patchsize)):\n","        for j in range(int(ncols/patchsize)):\n","            tocat = imgarray[i * patchsize:(i + 1)*patchsize,\n","                             j * patchsize:(j + 1)*patchsize, :]\n","            tocat = np.nan_to_num(tocat, nan = np.nanmedian(tocat))\n","            tocat[tocat < -1] = np.median(tocat)\n","            tocat = np.expand_dims(tocat, axis = 0)\n","            patchsamples = np.concatenate((patchsamples, tocat),axis=0)\n","    return patchsamples"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":566528,"status":"ok","timestamp":1654293666913,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"pvkyh4Z29ZNG","outputId":"d5dc60f4-1589-4dff-e766-db96acbe8f7c"},"outputs":[{"output_type":"stream","name":"stdout","text":["There are 1936 Training Patches in Sentinel-1 Raster of Size 256 x 256\n","There are 1936 Training Patches in Sentinel-2 Raster of Size 256 x 256\n"]}],"source":["# SETTING UP FULL IMAGE FOR SENTINEL-1 AND SENTINEL-2 DATASET\n","Xtrain_full_S1 = np.zeros(shape = (0, patch_size, patch_size, nband_S1), dtype = np.float32)\n","Xtrain_full_S2 = np.zeros(shape = (0, patch_size, patch_size, nband_S2), dtype = np.float32)\n","Ytrain_full = np.zeros(shape = (0, patch_size, patch_size, 1), dtype = np.uint8)\n","\n","# Sample of each Training Tile Systematically in a Gridwise Manner\n","train_areas_full = [\"ter\"]\n","\n","for area in train_areas_full:\n","    X_toadd_full_S1 = gridwise_sample(image_tiles_full_S1[area], patch_size)\n","    X_toadd_full_S2 = gridwise_sample(image_tiles_full_S2[area], patch_size)\n","    Y_toadd_full = gridwise_sample(label_tiles_full[area], patch_size)\n","    Xtrain_full_S1 = np.concatenate((Xtrain_full_S1, X_toadd_full_S1), axis = 0)\n","    Xtrain_full_S2 = np.concatenate((Xtrain_full_S2, X_toadd_full_S2), axis = 0)\n","    Ytrain_full = np.concatenate((Ytrain_full, Y_toadd_full), axis = 0)\n","\n","# Encode all Non-Deforestation Classes as Class \"2\"\n","Ytrain_full[Ytrain_full!=1] = 2\n","print(\"There are %i Training Patches in Sentinel-1 Raster of Size 256 x 256\" % (Xtrain_full_S1.shape[0]))\n","print(\"There are %i Training Patches in Sentinel-2 Raster of Size 256 x 256\" % (Xtrain_full_S2.shape[0]))"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"executionInfo":{"elapsed":537,"status":"ok","timestamp":1654293667432,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"rTuPbJtWAFZx","outputId":"6c7a6eb1-9251-4824-dab8-3b092c7df7c4"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 288x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# VISUALIZING ONE RANDOM 256 x 256 PATCH I.E. IMAGE AGAINST ITS LABEL\n","patch_number_full = 451\n","# visualize_data(Xtrain_full_S1[patch_number_full], \"Training Patch \" + str(patch_number_full), 4, 4)\n","# visualize_data(Xtrain_full_S2[patch_number_full], \"Training Patch \" + str(patch_number_full), 4, 4)\n","visualize_labels(Ytrain_full[patch_number_full], 4, 4)"]},{"cell_type":"markdown","metadata":{"id":"QcvLFQEbHN-O"},"source":["# IMAGE DIVIDED INTO 16 GRID"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":50,"status":"ok","timestamp":1654293667438,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"awjCSZMkHNow","outputId":"cbda530f-a3d5-4219-d80b-a4111d720401"},"outputs":[{"output_type":"stream","name":"stdout","text":["NUMBER OF BANDS IN SENTINEL-1 RASTER: 2\n","NUMBER OF BANDS IN SENTINEL-2 RASTER: 4\n"]}],"source":["# SETTING UP THE PATCH SIZE AND IDENTIFICATION OF THE NUMBER OF BANDS\n","nband_S1_16_grid = image_tiles_S1_16_grid[\"R_0\"].shape[-1]\n","nband_S2_16_grid = image_tiles_S2_16_grid[\"R_0\"].shape[-1]\n","print(\"NUMBER OF BANDS IN SENTINEL-1 RASTER:\", nband_S1_16_grid)\n","print(\"NUMBER OF BANDS IN SENTINEL-2 RASTER:\", nband_S2_16_grid)"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":54837,"status":"ok","timestamp":1654293722244,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"tha-CnsCHWBv","outputId":"a5668434-496c-4f2d-f8a9-9ba91717ae30"},"outputs":[{"output_type":"stream","name":"stdout","text":["There are 1694 Training Patches in Sentinel-1 Raster of Size 256 x 256\n","There are 1694 Training Patches in Sentinel-2 Raster of Size 256 x 256\n"]}],"source":["# SETTING UP FULL IMAGE FOR SENTINEL-1 AND SENTINEL-2 DATASET\n","Xtrain_S1_16_grid = np.zeros(shape = (0, patch_size, patch_size, nband_S1_16_grid), dtype = np.float32)\n","Xtrain_S2_16_grid = np.zeros(shape = (0, patch_size, patch_size, nband_S2_16_grid), dtype = np.float32)\n","Ytrain_16_grid = np.zeros(shape = (0, patch_size, patch_size, 1), dtype = np.uint8)\n","\n","# Sample of each Training Tile Systematically in a Gridwise Manner\n","train_areas_16_grid = [\"R_0\", \"R_1\", \"R_2\", \"R_3\" ,\"R_4\", \"R_5\", \"R_6\", \"R_7\", \"R_8\", \"R_9\", \n","                       \"_10\", \"_11\", \"_13\", \"_15\" ,] # \"_12\" AND \"_14\" ARE KEPT FOR TEST SET \n","                       \n","for area in train_areas_16_grid:\n","    X_toadd_S1_16_grid = gridwise_sample(image_tiles_S1_16_grid[area], patch_size)\n","    X_toadd_S2_16_grid = gridwise_sample(image_tiles_S2_16_grid[area], patch_size)\n","    Y_toadd_16_grid = gridwise_sample(label_tiles_16_grid[area], patch_size)\n","    Xtrain_S1_16_grid = np.concatenate((Xtrain_S1_16_grid, X_toadd_S1_16_grid), axis = 0)\n","    Xtrain_S2_16_grid = np.concatenate((Xtrain_S2_16_grid, X_toadd_S2_16_grid), axis = 0)\n","    Ytrain_16_grid = np.concatenate((Ytrain_16_grid, Y_toadd_16_grid), axis = 0)\n","\n","# Encode all Non-Deforestation Classes as Class \"2\"\n","Ytrain_16_grid[Ytrain_16_grid!=1] = 2\n","print(\"There are %i Training Patches in Sentinel-1 Raster of Size 256 x 256\" % (Xtrain_S1_16_grid.shape[0]))\n","print(\"There are %i Training Patches in Sentinel-2 Raster of Size 256 x 256\" % (Xtrain_S2_16_grid.shape[0]))"]},{"cell_type":"markdown","metadata":{"id":"osLwjF3Bs5jH"},"source":["# 5. SETTING UP THE FULLY CONVOLUTIONAL NETWRK (FCN)"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"oXW0bDUOuHy-","executionInfo":{"status":"ok","timestamp":1654293727030,"user_tz":-120,"elapsed":4807,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# IMPORTING THE ESSENTIAL LIBRARIES\n","import tensorflow as tf\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Activation, BatchNormalization, Reshape\n","from tensorflow.keras import backend as K\n","from tensorflow.keras.optimizers import SGD, Adadelta\n","import tensorflow.python.keras.backend as K\n","sess = K.get_session()\n","from tensorflow.compat.v1.keras.backend import set_session"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"vTWSeWSaHz2q","executionInfo":{"status":"ok","timestamp":1654293727035,"user_tz":-120,"elapsed":38,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# IMPORTING THE ESSENTIAL LIBRARIES FOR UNET\n","from keras.models import Model\n","from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda\n","from keras import backend as K"]},{"cell_type":"code","execution_count":23,"metadata":{"id":"S0uTspIjKNnL","executionInfo":{"status":"ok","timestamp":1654293727037,"user_tz":-120,"elapsed":31,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# DEFINING A FUNCTION FOR CONVERTING GROUND TRUTH TO ONE-HOT ENCODING\n","def to_categorical_4d(y, nc):\n","    Y = np.zeros((y.shape[0],\n","                  y.shape[1],\n","                  y.shape[2],\n","                  nc),\n","                  dtype=np.int32)\n","    for h in range(y.shape[0]):\n","        for i in range(y.shape[1]):\n","            for j in range(y.shape[2]):\n","                if y[h, i, j, 0]!= 0:\n","                    Y[h, i, j, y[h, i, j, 0]-1] = 1\n","                else:\n","                    continue\n","    return Y"]},{"cell_type":"code","execution_count":24,"metadata":{"executionInfo":{"elapsed":461948,"status":"ok","timestamp":1654294188960,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"sG-ju8FyKNgX"},"outputs":[],"source":["# TRANSFORM TARGET ARRAYS TO ONE-HOT ENCODINGS\n","Ytrain_16_grid = to_categorical_4d(Ytrain_16_grid, 2)"]},{"cell_type":"markdown","metadata":{"id":"ouxayG_7gwwn"},"source":["# 6. DEFINING A FUNCTION FOR LATE FUSION BASED ON SEGNET ARCHITECTURE"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"Ub-d-Nk3EEYQ","executionInfo":{"status":"ok","timestamp":1654294207719,"user_tz":-120,"elapsed":219,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# IMPORT THE LIBRARIES ESSENTIAL FOR SEGNET\n","from keras.layers import Activation, Dense, BatchNormalization, Dropout, Conv2D, Conv2DTranspose, MaxPooling2D, UpSampling2D, Input, Reshape, Concatenate"]},{"cell_type":"code","execution_count":26,"metadata":{"id":"cCNnYPDPEASN","executionInfo":{"status":"ok","timestamp":1654294209600,"user_tz":-120,"elapsed":976,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# LATE FUSION ARCHITECTURE\n","def late_fusion_model(optimizer, nrows, ncols, nbands, nclasses):\n","\n","    # TAKING OUT BANDS VALUES FROM THE VARIABLE \"nbands\" INSIDE \"nbands1\" AND \"nbands2\"\n","    nbands1 = nbands[1]\n","    nbands2  = nbands[0]\n","\n","    # OPTICAL ENCODER\n","\n","    # OPTICAL ENCODER NUMBER 1\n","    img_input_1 = Input(shape = (nrows, ncols, nbands2), name = 'optical_input')\n","    x1 = Conv2D(64, (3, 3), padding = 'same', name = 'conv1x',strides = (1,1))(img_input_1)\n","    x1 = BatchNormalization(name = 'bn1x')(x1)\n","    x1 = Activation('relu')(x1)\n","    x1 = Conv2D(64, (3, 3), padding = 'same', name = 'conv2x')(x1)\n","    x1 = BatchNormalization(name = 'bn2x')(x1)\n","    x1 = Activation('relu')(x1)\n","    p1 = MaxPooling2D()(x1)\n","    \n","    # OPTICAL ENCODER NUMBER 2\n","    x2 = Conv2D(128, (3, 3), padding = 'same', name = 'conv3x')(p1)\n","    x2 = BatchNormalization(name = 'bn3x')(x2)\n","    x2 = Activation('relu')(x2)\n","    x2 = Conv2D(128, (3, 3), padding = 'same', name = 'conv4x')(x2)\n","    x2 = BatchNormalization(name = 'bn4x')(x2)\n","    x2 = Activation('relu')(x2)\n","    p2 = MaxPooling2D()(x2)\n","\n","    # OPTICAL ENCODER NUMBER 3\n","    x3 = Conv2D(256, (3, 3), padding = 'same', name = 'conv5x')(p2)\n","    x3 = BatchNormalization(name = 'bn5x')(x3)\n","    x3 = Activation('relu')(x3)\n","    x3 = Conv2D(256, (3, 3), padding = 'same', name = 'conv6x')(x3)\n","    x3 = BatchNormalization(name = 'bn6x')(x3)\n","    x3 = Activation('relu')(x3)\n","    x3 = Conv2D(256, (3, 3), padding = 'same', name = 'conv7x')(x3)\n","    x3 = BatchNormalization(name = 'bn7x')(x3)\n","    x3 = Activation('relu')(x3)\n","    p3 = MaxPooling2D()(x3)\n","\n","    # OPTICAL ENCODER NUMBER 4\n","    x4 = Conv2D(512, (3, 3), padding = 'same', name = 'conv8x')(p3)\n","    x4 = BatchNormalization(name = 'bn8x')(x4)\n","    x4 = Activation('relu')(x4)\n","    x4 = Conv2D(512, (3, 3), padding = 'same', name = 'conv9x')(x4)\n","    x4 = BatchNormalization(name = 'bn9x')(x4)\n","    x4 = Activation('relu')(x4)\n","    x4 = Conv2D(512, (3, 3), padding = 'same', name = 'conv10x')(x4)\n","    x4 = BatchNormalization(name = 'bn10x')(x4)\n","    x4 = Activation('relu')(x4)\n","    p4 = MaxPooling2D()(x4)\n","    \n","    # OPTICAL ENCODER NUMBER 5\n","    x5 = Conv2D(512, (3, 3), padding = 'same', name = 'conv11x')(p4)\n","    x5 = BatchNormalization(name = 'bn11x')(x5)\n","    x5 = Activation('relu')(x5)\n","    x5 = Conv2D(512, (3, 3), padding = 'same', name = 'conv12x')(x5)\n","    x5 = BatchNormalization(name = 'bn12x')(x5)\n","    x5 = Activation('relu')(x5)\n","    x5 = Conv2D(512, (3, 3), padding = 'same', name = 'conv13x')(x5)\n","    x5 = BatchNormalization(name='bn13x')(x5)\n","    x5 = Activation('relu')(x5)\n","    p5 = MaxPooling2D()(x5)\n","\n","    x6 = Dense(1024, activation = 'relu', name = 'fc1x')(p5)\n","    x7 = Dense(1024, activation = 'relu', name = 'fc2x')(x6)\n","    \n","    # OPTICAL DECODER \n","\n","    # OPTICAL DECODER NUMBER 5\n","    x8 = UpSampling2D()(x7)\n","    x8 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv1x')(x8)\n","    x8 = BatchNormalization(name = 'bn14x')(x8)\n","    x8 = Activation('relu')(x8)\n","    x8 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv2x')(x8)\n","    x8 = BatchNormalization(name = 'bn15x')(x8)\n","    x8 = Activation('relu')(x8)\n","    x8 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv3x')(x8)\n","    x8 = BatchNormalization(name = 'bn16x')(x8)\n","    p8 = Activation('relu')(x8)\n","    \n","    # OPTICAL DECODER NUMBER 4\n","    x9 = UpSampling2D()(p8)\n","    x9 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv4x')(x9)\n","    x9 = BatchNormalization(name = 'bn17x')(x9)\n","    x9 = Activation('relu')(x9)\n","    x9 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv5x')(x9)\n","    x9 = BatchNormalization(name = 'bn18x')(x9)\n","    x9 = Activation('relu')(x9)\n","    x9 = Conv2DTranspose(256, (3, 3), padding = 'same', name = 'deconv6x')(x9)\n","    x9 = BatchNormalization(name = 'bn19x')(x9)\n","    p9 = Activation('relu')(x9)\n","\n","    # OPTICAL DECODER NUMBER 3\n","    x10 = UpSampling2D()(p9)\n","    x10 = Conv2DTranspose(256, (3, 3), padding = 'same', name = 'deconv7x')(x10)\n","    x10 = BatchNormalization(name = 'bn20x')(x10)\n","    x10 = Activation('relu')(x10)\n","    x10 = Conv2DTranspose(256, (3, 3), padding = 'same', name = 'deconv8x')(x10)\n","    x10 = BatchNormalization(name = 'bn21x')(x10)\n","    x10 = Activation('relu')(x10)\n","    x10 = Conv2DTranspose(128, (3, 3), padding = 'same', name = 'deconv9x')(x10)\n","    x10 = BatchNormalization(name = 'bn22x')(x10)\n","    p10 = Activation('relu')(x10)\n","\n","    # OPTICAL DECODER NUMBER 2\n","    x11 = UpSampling2D()(p10)\n","    x11 = Conv2DTranspose(128, (3, 3), padding = 'same', name = 'deconv10x')(x11)\n","    x11 = BatchNormalization(name = 'bn23x')(x11)\n","    x11 = Activation('relu')(x11)\n","    x11 = Conv2DTranspose(64, (3, 3), padding = 'same', name = 'deconv11x')(x11)\n","    x11 = BatchNormalization(name = 'bn24x')(x11)\n","    p11 = Activation('relu')(x11)\n","    \n","    # OPTICAL DECODER NUMBER 1\n","    x12 = UpSampling2D()(p11)\n","    p12 = Conv2DTranspose(64, (3, 3), padding = 'same', name = 'deconv12x')(x12)\n","    # x12 = BatchNormalization(name = 'bn25x')(x12)\n","    # x12 = Activation('relu')(x12)\n","    # x12 = Conv2DTranspose(1, (3, 3), padding = 'same', name = 'deconv13x')(x12)\n","    # x12 = BatchNormalization(name = 'bn26x')(x12)\n","    # p12 = Activation('relu')(x12)\n","    # p13 = Convolution2D(filters = nclasses, kernel_size=(1, 1))(p12)\n","    # output1 = Conv2D(nclasses, (1, 1), activation = 'softmax')(p12)\n","\n","\n","\n","\n","    # RADAR ENCODER\n","\n","    # RADAR ENCODER NUMBER 1\n","    img_input_2 = Input(shape = (nrows, ncols, nbands1), name = 'radar_input')\n","    y1 = Conv2D(64, (3, 3), padding = 'same', name = 'conv1y',strides = (1,1))(img_input_2)\n","    y1 = BatchNormalization(name = 'bn1y')(y1)\n","    y1 = Activation('relu')(y1)\n","    y1 = Conv2D(64, (3, 3), padding = 'same', name = 'conv2y')(y1)\n","    y1 = BatchNormalization(name = 'bn2y')(y1)\n","    y1 = Activation('relu')(y1)\n","    a1 = MaxPooling2D()(y1)\n","    \n","    # RADAR ENCODER NUMBER 2\n","    y2 = Conv2D(128, (3, 3), padding = 'same', name = 'conv3y')(a1)\n","    y2 = BatchNormalization(name = 'bn3y')(y2)\n","    y2 = Activation('relu')(y2)\n","    y2 = Conv2D(128, (3, 3), padding = 'same', name = 'conv4y')(y2)\n","    y2 = BatchNormalization(name = 'bn4y')(y2)\n","    y2 = Activation('relu')(y2)\n","    a2 = MaxPooling2D()(y2)\n","\n","    # RADAR ENCODER NUMBER 3\n","    y3 = Conv2D(256, (3, 3), padding = 'same', name = 'conv5y')(a2)\n","    y3 = BatchNormalization(name = 'bn5y')(y3)\n","    y3 = Activation('relu')(y3)\n","    y3 = Conv2D(256, (3, 3), padding = 'same', name = 'conv6y')(y3)\n","    y3 = BatchNormalization(name = 'bn6y')(y3)\n","    y3 = Activation('relu')(y3)\n","    y3 = Conv2D(256, (3, 3), padding = 'same', name = 'conv7y')(y3)\n","    y3 = BatchNormalization(name = 'bn7y')(y3)\n","    y3 = Activation('relu')(y3)\n","    a3 = MaxPooling2D()(y3)\n","\n","    # RADAR ENCODER NUMBER 4\n","    y4 = Conv2D(512, (3, 3), padding = 'same', name = 'conv8y')(a3)\n","    y4 = BatchNormalization(name = 'bn8y')(y4)\n","    y4 = Activation('relu')(y4)\n","    y4 = Conv2D(512, (3, 3), padding = 'same', name = 'conv9y')(y4)\n","    y4 = BatchNormalization(name = 'bn9y')(y4)\n","    y4 = Activation('relu')(y4)\n","    y4 = Conv2D(512, (3, 3), padding = 'same', name = 'conv10y')(y4)\n","    y4 = BatchNormalization(name = 'bn10y')(y4)\n","    y4 = Activation('relu')(y4)\n","    a4 = MaxPooling2D()(y4)\n","    \n","    # RADAR ENCODER NUMBER 5\n","    y5 = Conv2D(512, (3, 3), padding = 'same', name = 'conv11y')(a4)\n","    y5 = BatchNormalization(name = 'bn11y')(y5)\n","    y5 = Activation('relu')(y5)\n","    y5 = Conv2D(512, (3, 3), padding = 'same', name = 'conv12y')(y5)\n","    y5 = BatchNormalization(name = 'bn12y')(y5)\n","    y5 = Activation('relu')(y5)\n","    y5 = Conv2D(512, (3, 3), padding = 'same', name = 'conv13y')(y5)\n","    y5 = BatchNormalization(name='bn13y')(y5)\n","    y5 = Activation('relu')(y5)\n","    a5 = MaxPooling2D()(y5)\n","\n","    y6 = Dense(1024, activation = 'relu', name = 'fc1y')(a5)\n","    y7 = Dense(1024, activation = 'relu', name = 'fc2y')(y6)\n","    \n","    # RADAR DECODER \n","\n","    # RADAR DECODER NUMBER 5\n","    y8 = UpSampling2D()(y7)\n","    y8 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv1y')(y8)\n","    y8 = BatchNormalization(name = 'bn14y')(y8)\n","    y8 = Activation('relu')(y8)\n","    y8 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv2y')(y8)\n","    y8 = BatchNormalization(name = 'bn15y')(y8)\n","    y8 = Activation('relu')(y8)\n","    y8 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv3y')(y8)\n","    y8 = BatchNormalization(name = 'bn16y')(y8)\n","    a8 = Activation('relu')(y8)\n","    \n","    # RADAR DECODER NUMBER 4\n","    y9 = UpSampling2D()(a8)\n","    y9 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv4y')(y9)\n","    y9 = BatchNormalization(name = 'bn17y')(y9)\n","    y9 = Activation('relu')(y9)\n","    y9 = Conv2DTranspose(512, (3, 3), padding = 'same', name = 'deconv5y')(y9)\n","    y9 = BatchNormalization(name = 'bn18y')(y9)\n","    y9 = Activation('relu')(y9)\n","    y9 = Conv2DTranspose(256, (3, 3), padding = 'same', name = 'deconv6y')(y9)\n","    y9 = BatchNormalization(name = 'bn19y')(y9)\n","    a9 = Activation('relu')(y9)\n","\n","    # RADAR DECODER NUMBER 3\n","    y10 = UpSampling2D()(a9)\n","    y10 = Conv2DTranspose(256, (3, 3), padding = 'same', name = 'deconv7y')(y10)\n","    y10 = BatchNormalization(name = 'bn20y')(y10)\n","    y10 = Activation('relu')(y10)\n","    y10 = Conv2DTranspose(256, (3, 3), padding = 'same', name = 'deconv8y')(y10)\n","    y10 = BatchNormalization(name = 'bn21y')(y10)\n","    y10 = Activation('relu')(y10)\n","    y10 = Conv2DTranspose(128, (3, 3), padding = 'same', name = 'deconv9y')(y10)\n","    y10 = BatchNormalization(name = 'bn22y')(y10)\n","    a10 = Activation('relu')(y10)\n","\n","    # RADAR DECODER NUMBER 2\n","    y11 = UpSampling2D()(a10)\n","    y11 = Conv2DTranspose(128, (3, 3), padding = 'same', name = 'deconv10y')(y11)\n","    y11 = BatchNormalization(name = 'bn23y')(y11)\n","    y11 = Activation('relu')(y11)\n","    y11 = Conv2DTranspose(64, (3, 3), padding = 'same', name = 'deconv11y')(y11)\n","    y11 = BatchNormalization(name = 'bn24y')(y11)\n","    a11 = Activation('relu')(y11)\n","    \n","    # RADAR DECODER NUMBER 1\n","    y12 = UpSampling2D()(a11)\n","    a12 = Conv2DTranspose(64, (3, 3), padding = 'same', name = 'deconv12y')(y12)\n","    # y12 = BatchNormalization(name = 'bn25y')(y12)\n","    # y12 = Activation('relu')(y12)\n","    # y12 = Conv2DTranspose(1, (3, 3), padding = 'same', name = 'deconv13y')(y12)\n","    # y12 = BatchNormalization(name = 'bn26y')(y12)\n","    # a12 = Activation('relu')(y12)\n","    # a13 = Convolution2D(filters = nclasses, kernel_size=(1, 1))(a12)\n","    # output2 = Conv2D(nclasses, (1, 1), activation = 'softmax')(a12)\n","\n","\n","\n","\n","    # PERFORMING LATE FUSION OF OPTICAL AND RADAR SENSOR\n","    late_fusion = Concatenate()([p12, a12])\n","\n","    # PERFORMING BATCH NORMALIZATION ON CONCATENATED TRANSPOSED CONVOLUTION\n","    lf1 = BatchNormalization(name = 'bn25xy')(late_fusion)\n","    lf1 = Activation('relu')(lf1)\n","    lf1 = Conv2D(filters = nclasses, kernel_size = (1, 1), padding = 'same', name = 'convxy', strides = (1,1))(lf1)\n"," \n","    # FINAL SOFTMAX LAYER FOR SEMANTIC SEGMENTATION\n","    outputs = Conv2D(nclasses, (1, 1), activation = 'softmax')(lf1)\n","\n","    # SETTING UP THE MODEL INPUT AND OUTPUT PARAMETERS\n","    model = Model(inputs = [img_input_1, img_input_2], outputs = outputs) # I THINK THAT THIS NEEDS TO BE CHANGED\n","    \n","    # NOTE: COMPILE THE MODEL IN THE MAIN PROGRAM TO MAKE IT ASY TO TEST WITH VARIOUS LOSS FUNCTION\n","    model.compile(loss = \"categorical_crossentropy\", metrics = ['accuracy'], optimizer = optimizer)\n","\n","    # PRINTING THE SUMMARY OF THE ENTIRE SEGNET MODEL\n","    # model.summary()\n","    \n","    return model"]},{"cell_type":"markdown","metadata":{"id":"6DHD2blUJdYk"},"source":["# DIVIDING THE DATASET INTO TRAINING, VALIDATION AND TEST SET "]},{"cell_type":"code","execution_count":27,"metadata":{"id":"McR3u8iBzmVB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654294210714,"user_tz":-120,"elapsed":282,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"e7788707-a297-4d60-b9d4-e2ea423f9a38"},"outputs":[{"output_type":"stream","name":"stdout","text":["TRAINING AND VALIDATION SET OF SENTINEL-1 RASTER:\n","X-TRAIN: 1439 PATCHES\n","Y-TRAIN: 1439 PATCHES\n","\n","X-VALIDATION: 255 PATCHES\n","Y-VALIDATION: 255 PATCHES\n"]}],"source":["# DIVIDING THE SENTINEL-1 DATASET INTO TRAINING AND VALIDATION SET\n","from sklearn.model_selection import train_test_split\n","x_train_S1, x_val_S1, y_train_S1, y_val_S1 = train_test_split(Xtrain_S1_16_grid, Ytrain_16_grid, test_size = 0.15, random_state = 1)\n","\n","print(\"TRAINING AND VALIDATION SET OF SENTINEL-1 RASTER:\")\n","print(\"X-TRAIN:\", x_train_S1.shape[0], \"PATCHES\")\n","print(\"Y-TRAIN:\", y_train_S1.shape[0], \"PATCHES\")\n","print(\"\\nX-VALIDATION:\", x_val_S1.shape[0], \"PATCHES\")\n","print(\"Y-VALIDATION:\", y_val_S1.shape[0], \"PATCHES\")"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"2Yoh8RM1InVG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654294211640,"user_tz":-120,"elapsed":945,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"9a63c070-6031-40fe-ed4e-9396d2c7277d"},"outputs":[{"output_type":"stream","name":"stdout","text":["TRAINING AND VALIDATION SET OF SENTINEL-2 RASTER:\n","X-TRAIN: 1439 PATCHES\n","Y-TRAIN: 1439 PATCHES\n","\n","X-VALIDATION: 255 PATCHES\n","Y-VALIDATION: 255 PATCHES\n"]}],"source":["# DIVIDING THE SENTINEL-2 DATASET INTO TRAINING AND VALIDATION SET\n","from sklearn.model_selection import train_test_split\n","x_train_S2, x_val_S2, y_train_S2, y_val_S2 = train_test_split(Xtrain_S2_16_grid, Ytrain_16_grid, test_size = 0.15, random_state = 1)\n","\n","print(\"TRAINING AND VALIDATION SET OF SENTINEL-2 RASTER:\")\n","print(\"X-TRAIN:\", x_train_S2.shape[0], \"PATCHES\")\n","print(\"Y-TRAIN:\", y_train_S2.shape[0], \"PATCHES\")\n","print(\"\\nX-VALIDATION:\", x_val_S2.shape[0], \"PATCHES\")\n","print(\"Y-VALIDATION:\", y_val_S2.shape[0], \"PATCHES\")"]},{"cell_type":"markdown","metadata":{"id":"MzED-GapJ_f5"},"source":["# 10. SETTING UP THE MODEL PARAMETERS"]},{"cell_type":"code","execution_count":29,"metadata":{"executionInfo":{"elapsed":13,"status":"ok","timestamp":1654294211643,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"YeJW4i-uB9-J"},"outputs":[],"source":["# DEFINING A FUNCTION FOR TRAINING THE MODEL USING \"MODEL CHECKPOINT\", \"EARLY STOPPING\" AND \"CSV LOGGER\"\n","from keras.callbacks import ModelCheckpoint\n","from keras.callbacks import EarlyStopping\n","from keras.callbacks import CSVLogger\n","import imp, h5py\n","imp.reload(h5py)\n","\n","model_fname = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/CLOUD_TRAINED_MODEL/2020/LATE_FUSION/LF_2020_SEGNET_Weights_Improvement_{epoch:02d}_{val_acc:.2f}.hdf5\"\n","csv_logger_save = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/CLOUD_TRAINED_MODEL/2020/LATE_FUSION/LF_2020_SEGNET_LOGS.csv\"\n","\n","early_stop = EarlyStopping(monitor = 'val_loss', mode = 'min', verbose = 1, patience = 100)\n","checkpoint = ModelCheckpoint(model_fname, monitor = \"val_acc\", verbose = 1, save_best_only = True, mode = \"max\")\n","csv_logger = CSVLogger(csv_logger_save, separator = \",\", append = False)\n","callbacks_list = [early_stop, checkpoint, csv_logger]\n","\n","def train(model, data, labels):\n","    hist = model.fit(x = data,\n","                     y = labels,\n","                     epochs = number_epochs,\n","                     batch_size = 10,\n","                     verbose = 2, \n","                     validation_data = ([x_val_S2, x_val_S1], y_val_S2),\n","                     callbacks = callbacks_list)\n","    return hist"]},{"cell_type":"code","execution_count":30,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1654294213186,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"TsW-jdELKNpO"},"outputs":[],"source":["# SETTING UP THE BASIC PARAMETERS OF THE FULLY CONVOLUTIONAL NETWORK (FCN)\n","number_bands = [4, 2]\n","number_classes = 2\n","number_epochs = 250  "]},{"cell_type":"code","execution_count":31,"metadata":{"executionInfo":{"elapsed":13,"status":"ok","timestamp":1654294215298,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"nbHGxYAaH5m8"},"outputs":[],"source":["# DEFINING A FUNCTION FOR \"jacard_coef\"\n","def jacard_coef(y_true, y_pred):\n","    y_true_f = K.flatten(y_true)\n","    y_pred_f = K.flatten(y_pred)\n","    intersection = K.sum(y_true_f * y_pred_f)\n","    return (intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + 1.0)"]},{"cell_type":"code","execution_count":32,"metadata":{"executionInfo":{"elapsed":16,"status":"ok","timestamp":1654294217002,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"eaKwTDqhKNd_"},"outputs":[],"source":["# DEFINING A FUNCTION FOR \"recall_m\"\n","def recall_m(y_true, y_pred):\n","    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n","    recall = true_positives / (possible_positives + K.epsilon())\n","    return recall"]},{"cell_type":"code","execution_count":33,"metadata":{"executionInfo":{"elapsed":30,"status":"ok","timestamp":1654294217401,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"f2_NSXmj7DKM"},"outputs":[],"source":["# DEFINING A FUNCTION FOR \"precision_m\"\n","def precision_m(y_true, y_pred):\n","    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n","    precision = true_positives / (predicted_positives + K.epsilon())\n","    return precision"]},{"cell_type":"code","execution_count":34,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1654294217405,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"NgWO4Mzt7ERM"},"outputs":[],"source":["# DEFININF A FUNCTION FOR \"f1_m\"\n","def f1_m(y_true, y_pred):\n","    precision = precision_m(y_true, y_pred)\n","    recall = recall_m(y_true, y_pred)\n","    return 2*((precision*recall)/(precision+recall+K.epsilon()))"]},{"cell_type":"code","execution_count":35,"metadata":{"executionInfo":{"elapsed":1945,"status":"ok","timestamp":1654294220221,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"KnKnNtRTvrDc"},"outputs":[],"source":["# DEFINING THE FULLY CONVOLUTIONAL NETWORK (FCN) MODEL SPECIFICALLY THE U-NET\n","opt = tf.keras.optimizers.SGD(learning_rate = 0.01, momentum = 0.9)\n","fcn_sar = late_fusion_model(opt, patch_size, patch_size, number_bands, number_classes)\n","fcn_sar.compile(optimizer = opt, loss = 'binary_crossentropy', metrics = ['acc', f1_m,precision_m, recall_m]) #'adam'"]},{"cell_type":"markdown","metadata":{"id":"4WdvzycUKEOw"},"source":["# 11. TIME TO RUN THE MODEL"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":365},"id":"N0dQsxZo7pjp","outputId":"1df4b50c-aec0-4b89-9c9b-298bc7651353","executionInfo":{"status":"error","timestamp":1654290530821,"user_tz":-120,"elapsed":24282,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/250\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-36-ead0bac00262>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# TIME TO TRAIN AND SAVE THE FCN MODEL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfcn_sar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx_train_S2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train_S1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_S2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mfcn_sar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_fname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-29-14416631034a>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, data, labels)\u001b[0m\n\u001b[1;32m     21\u001b[0m                      \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m                      \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx_val_S2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_val_S1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val_S2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m                      callbacks = callbacks_list)\n\u001b[0m\u001b[1;32m     24\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m 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149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    978\u001b[0m         \u001b[0;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0;31m# stateless function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 980\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    981\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m       \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiltered_flat_args\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2955\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2956\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2957\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2959\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1852\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1854\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1855\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1856\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    502\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    505\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["# TIME TO TRAIN AND SAVE THE FCN MODEL                                              \n","history = train(fcn_sar, [x_train_S2, x_train_S1], y_train_S2)         \n","fcn_sar.save(model_fname)                                                                   "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lvUoe_mT72LE"},"outputs":[],"source":["# PLOTTING THE TRAINING AND VALIDATION LOSS OF THE MODEL\n","from matplotlib import pyplot as plt\n","loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","epochs = range(1, len(loss) + 1)\n","plt.plot(epochs, loss, 'y', label = 'TRAINING LOSS')\n","plt.plot(epochs, val_loss, 'r', label = 'VALIDATION LOSS')\n","plt.title('TRANING AND VALIDATION LOSS')\n","plt.xlabel('EPOCHS')\n","plt.ylabel('LOSS')\n","plt.legend()\n","plt.show()\n","\n","# PLOTTING THE TRAINING AND VALIDATION ACCURACY OF THE MODEL\n","accuracy = history.history['acc']\n","validation_accuracy = history.history['val_acc']\n","epochs_2 = range(1, len(accuracy) + 1)\n","plt.plot(epochs_2, accuracy, 'y', label = 'TRAINING ACCURACY')\n","plt.plot(epochs_2, validation_accuracy, 'r', label = 'VALIDATION ACCURACY')\n","plt.title('TRANING AND VALIDATION ACCURACY')\n","plt.xlabel('EPOCHS')\n","plt.ylabel('LOSS')\n","plt.legend()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"7tmRxRUjmKeY"},"source":["# PREDICTION ON TRAINING, VALIDATION AND TEST SET USING THE TRAINED WEIGHTS"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OraJrGnDyb8r"},"outputs":[],"source":["fcn_sar.load_weights(\"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/CLOUD_TRAINED_MODEL/2020/LATE_FUSION/LF_2020_SEGNET_Weights_Improvement_31_0.90.hdf5\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":37367,"status":"ok","timestamp":1653336061367,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"ZMErkKbhyedB","outputId":"0e255384-5c17-47f9-92c6-2e056b720ffc"},"outputs":[{"output_type":"stream","name":"stdout","text":["45/45 [==============================] - 36s 447ms/step - loss: 0.0556 - acc: 0.9769 - f1_m: 0.9769 - precision_m: 0.9769 - recall_m: 0.9769\n","________________________\n","OVERALL ACCURACY:       |   97.69  |\n","F1 SCORE:               |   97.69  |\n","PRECISION:              |   97.69  |\n","RECALL:                 |   97.69  |\n","________________________\n"]}],"source":["# EVALUATING THE MODEL PERFORMANCE ON TRAINING SET\n","res = fcn_sar.evaluate([x_train_S2, x_train_S1], y_train_S2)\n","print('________________________')\n","print('OVERALL ACCURACY:       |   {:.2f}  |'.format(res[1]*100))\n","print('F1 SCORE:               |   {:.2f}  |'.format(res[2]*100))\n","print('PRECISION:              |   {:.2f}  |'.format(res[3]*100))\n","print('RECALL:                 |   {:.2f}  |'.format(res[4]*100))\n","print('________________________')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NfuuSb2xygDf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1653336064956,"user_tz":-120,"elapsed":3598,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"9d2d125d-1d09-414a-814a-dba6d820c151"},"outputs":[{"output_type":"stream","name":"stdout","text":["8/8 [==============================] - 3s 402ms/step - loss: 0.4205 - acc: 0.9034 - f1_m: 0.9034 - precision_m: 0.9034 - recall_m: 0.9034\n","________________________\n","OVERALL ACCURACY:       |   90.34  |\n","F1 SCORE:               |   90.34  |\n","PRECISION:              |   90.34  |\n","RECALL:                 |   90.34  |\n","________________________\n"]}],"source":["# EVALUATING THE MODEL PERFORMANCE ON VALIDATION SET\n","res_2 = fcn_sar.evaluate([x_val_S2, x_val_S1], y_val_S2)\n","print('________________________')\n","print('OVERALL ACCURACY:       |   {:.2f}  |'.format(res_2[1]*100))\n","print('F1 SCORE:               |   {:.2f}  |'.format(res_2[2]*100))\n","print('PRECISION:              |   {:.2f}  |'.format(res_2[3]*100))\n","print('RECALL:                 |   {:.2f}  |'.format(res_2[4]*100))\n","print('________________________')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Q0_u5SusyhYU"},"outputs":[],"source":["# # EVALUATING THE MODEL PERFORMANCE ON TEST SET \n","# res_3 = fcn_sar.evaluate([x_test_S2, x_test_S1], y_test_GT)\n","# print('________________________')\n","# print('OVERALL ACCURACY:       |   {:.2f}  |'.format(res_3[1]*100))\n","# print('F1 SCORE:               |   {:.2f}  |'.format(res_3[2]*100))\n","# print('PRECISION:              |   {:.2f}  |'.format(res_3[3]*100))\n","# print('RECALL:                 |   {:.2f}  |'.format(res_3[4]*100))\n","# print('________________________')"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"By31lx3Wmn1w","executionInfo":{"status":"ok","timestamp":1654294230888,"user_tz":-120,"elapsed":209,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# DEFINING A FUNCTION TO PERFORM PREDICTION ON TEST SET\n","def evaluate_predictions(Xtest, w8fname, cut, overlap, Ytest, model_builder):\n","    Ytest = Ytest.copy()\n","    Ytest[Ytest!=2] = 0\n","    Ytest[Ytest==2] = 1\n","    ncols, nrows, nbands2 = Xtest[0].shape\n","    ncols, nrows, nbands1 = Xtest[1].shape\n","    Xtest[0] = np.expand_dims(Xtest[0], axis=0)\n","    Xtest[1] = np.expand_dims(Xtest[1], axis=0)    \n","    total_map = np.zeros((nrows, ncols), dtype=np.uint8)\n","    first = True\n","    last = False\n","    if nrows%cut==0:\n","        numstrips = int(nrows/cut)\n","    else:\n","        numstrips = int(nrows/cut) + 1\n","    for i in range(numstrips):\n","        print(\"Strip number: %d\" % i)\n","        if first:\n","            striptop = 0\n","            stripbottom = overlap\n","            height = cut\n","            X_sub2 = Xtest[0][:, cut*i-striptop:(cut*i)+height+stripbottom, :, :]\n","            X_sub1 = Xtest[1][:, cut*i-striptop:(cut*i)+height+stripbottom, :, :]\n","            first = False\n","        elif (not first) and (cut*(i+1)+1+overlap < nrows):\n","            striptop = overlap\n","            stripbottom = overlap\n","            height = cut\n","            X_sub2 = Xtest[0][:, cut*i-striptop:(cut*i)+height+stripbottom, :, :]\n","            X_sub1 = Xtest[1][:, cut*i-striptop:(cut*i)+height+stripbottom, :, :]\n","        else:\n","            print(\"Last hit!\")\n","            striptop = overlap\n","            stripbottom = 0\n","            height = nrows - cut*i\n","            if (striptop+height)%(4)!=0:\n","                height = height*4\n","            X_sub2 = Xtest[0][:, -(striptop+height):, :, :]\n","            X_sub1 = Xtest[1][:, -(striptop+height):, :, :]\n","            last = True        \n","        sub_nrows = X_sub1.shape[1]\n","        sub_ncols = X_sub1.shape[2]\n","        model = model_builder(opt, sub_nrows, sub_ncols, [nbands2,nbands1], number_classes)\n","        model.load_weights(w8fname)\n","        sub_ns, sub_nb, _, __ = X_sub1.shape\n","        cmap = model.predict_on_batch([X_sub2,X_sub1])\n","        cmap = np.argmax(cmap[0], axis=2)\n","        if not last:\n","            total_map[cut*i:cut*i+height, :] = cmap[striptop:striptop+height, 0:total_map.shape[1]]\n","        else:\n","            total_map[-(height):, :] = cmap[-(height):, 0:total_map.shape[1]]\n","    return total_map"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"7Dd9h9jTCYBL","executionInfo":{"status":"ok","timestamp":1654294232418,"user_tz":-120,"elapsed":288,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# IMAGES TILES FROM FULL RASTER\n","full_xtest_S1 = image_tiles_full_S1[\"ter\"]\n","full_xtest_S2 = image_tiles_full_S2[\"ter\"]\n","full_ytest = label_tiles_full[\"ter\"]"]},{"cell_type":"markdown","metadata":{"id":"YTRuYm22pna7"},"source":["# TRANSFORMATIONS AND PROJECTIONS OF THE INPUT RASTER"]},{"cell_type":"code","execution_count":38,"metadata":{"id":"4mcGLS28pplD","executionInfo":{"status":"ok","timestamp":1654294234780,"user_tz":-120,"elapsed":311,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# PUTTING THE TRANSFORMATION AND PROJECTION OF THE INPUT RASTER WITHIN VARIABLE FOR SENTINEL-1\n","ds = gdal.Open(\"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_FULL/raster.tif\")\n","gt = ds.GetGeoTransform()\n","proj = ds.GetProjection()"]},{"cell_type":"code","execution_count":39,"metadata":{"id":"BHCuOOfzwTMK","executionInfo":{"status":"ok","timestamp":1654294235754,"user_tz":-120,"elapsed":8,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["import string\n","from matplotlib import colors\n","from sklearn.metrics import f1_score, precision_score, recall_score\n","\n","# SET COLOUR CODES OF THE CLASSES\n","deforestation = [253, 231, 36]\n","non_deforestation = [32, 144, 140]\n","ccolors = (deforestation, non_deforestation)\n","NROWS, NCOLS, NBANDS2 = full_xtest_S2.shape\n","NROWS, NCOLS, NBANDS1 = full_xtest_S1.shape\n","\n","\"\"\"\n","    Print the classified map.\n","    Keyword arguments:\n","    predictions -- the array output labels from the classification\n","    results\n","    idxarray -- the array of indices of the classified samples\n","    to be plotted.\n","    filename -- the name (with the directory) of the file where to\n","    write the map.\n","    ccolors -- an array of tuples containing rgb values for the classes\n","    Both arrays should match and be arranged accordingly. And the\n","    index of the color should match the value of the of prediction.\n","\"\"\"\n","\n","def print_map(predictions, idxarray, filename = None, title = None, ccolors = None):\n","    if predictions.size != idxarray.shape[0]:\n","        raise ValueError(\"There should be the same number of \" +\n","                         \"predictions and indices.\")\n","    nrows, ncols = np.max(idxarray[:, 0])+1, np.max(idxarray[:, 1])+1\n","    classifiedmap = np.zeros((nrows, ncols, 3), dtype=\"uint8\")\n","    idx = 0\n","    for prediction in predictions:\n","        classifiedmap[idxarray[idx, 0],\n","                      idxarray[idx, 1]] = np.asarray(ccolors[prediction])\n","        idx += 1\n","    if filename is None:\n","        fig = plt.figure()\n","        plt.imshow(classifiedmap)\n","        if title:\n","            plt.suptitle(title)\n","        plt.show()\n","    else:\n","        output_raster = gdal.GetDriverByName('GTiff').Create(filename, NCOLS, NROWS, number_classes, gdal.GDT_Byte)\n","        output_raster.SetGeoTransform(gt)\n","        output_raster.SetProjection(proj)\n","        \n","        for i in range(number_classes):\n","            output_raster.GetRasterBand(i+1).WriteArray(classifiedmap.astype(np.uint8)[:, :, i])"]},{"cell_type":"code","execution_count":40,"metadata":{"id":"tgkAJU3twshv","executionInfo":{"status":"ok","timestamp":1654294238650,"user_tz":-120,"elapsed":190,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["\"\"\"Compute the Confusion Matrix\n","\n","    Keyword arguments:\n","    prediction -- the array output labels from the classification\n","    results\n","    labels -- the  array of \"true\" labels from the test set\n","    Both arrays should match, be arranged accordingly, and\n","    have non-negative values.\n","\"\"\"\n","\n","def get_confusion_matrix(predictions, labels):\n","    classes = np.unique(labels)\n","    nbclasses = classes.size\n","    if labels.size != predictions.size:\n","        raise ValueError(\"There should be the same number of \" +\n","                         \"predictions and labels.\")\n","    merged = np.concatenate((predictions.reshape(predictions.size, 1),\n","                             labels.reshape(labels.size, 1)), axis=1)\n","    CM = np.zeros((classes[-1] + 1, classes[-1] + 1))\n","    for c1 in classes:\n","        for c2 in classes:\n","            CM[c1, c2] = np.sum(np.logical_and(merged[:, 1] == c1,\n","                                               merged[:, 0] == c2))\n","    return CM"]},{"cell_type":"code","execution_count":41,"metadata":{"id":"HSOPNbYjwymV","executionInfo":{"status":"ok","timestamp":1654294239582,"user_tz":-120,"elapsed":6,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["\"\"\"Utility function to create cartesian image coordinates.\"\"\"\n","\n","def cartesian(arrays, out=None):\n","    arrays = [np.asarray(x) for x in arrays]\n","    dtype = arrays[0].dtype\n","    n = np.prod([x.size for x in arrays])\n","    if out is None:\n","        out = np.zeros([n, len(arrays)], dtype=dtype)\n","    m = int(n / arrays[0].size)\n","    out[:, 0] = np.repeat(arrays[0], m)\n","    if arrays[1:]:\n","        cartesian(arrays[1:], out=out[0:m, 1:])\n","        for j in range(1, arrays[0].size):\n","            out[j*m:(j+1)*m, 1:] = out[0:m, 1:]\n","    return out"]},{"cell_type":"code","execution_count":42,"metadata":{"id":"_d9PKqGxNuek","colab":{"base_uri":"https://localhost:8080/"},"outputId":"ec2fc846-fed6-4c20-95be-d78c8e8e1a95","executionInfo":{"status":"ok","timestamp":1654294438543,"user_tz":-120,"elapsed":197392,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Strip number: 0\n","Strip number: 1\n","Strip number: 2\n","Strip number: 3\n","Strip number: 4\n","WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f7c86928f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","Strip number: 5\n","WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f7c8865f830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","Strip number: 6\n","Strip number: 7\n","Strip number: 8\n","Strip number: 9\n","Strip number: 10\n","Strip number: 11\n","Strip number: 12\n","Strip number: 13\n","Strip number: 14\n","Strip number: 15\n","Strip number: 16\n","Strip number: 17\n","Strip number: 18\n","Strip number: 19\n","Strip number: 20\n","Strip number: 21\n","Strip number: 22\n","Strip number: 23\n","Strip number: 24\n","Strip number: 25\n","Strip number: 26\n","Strip number: 27\n","Strip number: 28\n","Strip number: 29\n","Strip number: 30\n","Strip number: 31\n","Strip number: 32\n","Strip number: 33\n","Strip number: 34\n","Strip number: 35\n","Strip number: 36\n","Strip number: 37\n","Strip number: 38\n","Strip number: 39\n","Strip number: 40\n","Strip number: 41\n","Strip number: 42\n","Strip number: 43\n","Last hit!\n"]}],"source":["cut = 256\n","overlap = 0\n","weights_pretrained_FCN = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/CLOUD_TRAINED_MODEL/2020/LATE_FUSION/LF_2020_SEGNET_Weights_Improvement_31_0.90.hdf5\"\n","predictions_FCN = evaluate_predictions([full_xtest_S2, full_xtest_S1], weights_pretrained_FCN, cut, overlap, full_ytest, late_fusion_model)"]},{"cell_type":"code","execution_count":43,"metadata":{"id":"nL7_hmh0wyhv","colab":{"base_uri":"https://localhost:8080/","height":607},"executionInfo":{"status":"ok","timestamp":1654294440686,"user_tz":-120,"elapsed":2161,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"38350331-bdda-46c4-bf76-68178d360a5b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# PLOT REFERENCE MAP\n","norm_ccolors = np.array(ccolors)/255.0\n","fig = plt.figure(figsize=(15, 10))\n","ax = fig.add_subplot(1, 1, 1)\n","cmap = colors.ListedColormap(norm_ccolors)\n","bounds = [0,1,2]\n","norm = colors.BoundaryNorm(bounds, cmap.N)\n","labels = full_ytest[:, :, 0].copy()\n","labels[labels!=2] = 0\n","labels[labels==2] = 1\n","ax.imshow(labels, cmap = cmap, norm=norm, interpolation = \"nearest\", origin = \"upper\")\n","ax.set_title(\"GROUND TRUTH\")\n","rectangles = [matplotlib.patches.Rectangle((0, 0), 1, 1, color=norm_ccolors[r]) for r in range(norm_ccolors.shape[0])]\n","classes = [\"DEFORESTATION\", \"NON-DEFORESTATION\"]\n","#Create legend from custom artist/label lists\n","ax.legend(rectangles, classes)\n","plt.show()\n","labels = labels.astype(\"uint8\")"]},{"cell_type":"code","execution_count":44,"metadata":{"id":"8ad3pwemxEpD","colab":{"base_uri":"https://localhost:8080/","height":781},"executionInfo":{"status":"ok","timestamp":1654294695750,"user_tz":-120,"elapsed":255083,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"6cc7e722-a98f-4a49-a8cb-dee91b1af316"},"outputs":[{"output_type":"stream","name":"stdout","text":["ACCURACY METRICS OF SEGNET RESULTS:\n","\n","Overall Accuracy: 0.947001\n","F1 SCORE: 0.954083\n","PA of Deforestation: 0.924974\n","PA of Non-Deforestation: 0.960976\n","User Accuracy of Deforestation: 0.924974\n","User Accuracy of Non-Deforestation: 0.960976\n","Producer Accuracy of Deforestation: 0.937649\n","Producer Accuracy of Non-Deforestation: 0.952805\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# EVALUATE SEGNET BASED LATE FUSION\n","predictions = predictions_FCN\n","mapidx = cartesian([range(predictions.shape[0]), range(predictions.shape[1])])\n","CM = get_confusion_matrix(predictions, labels)\n","OA = np.sum(np.diag(CM))/predictions.size\n","PA_bldg = ((predictions[labels==0]==labels[labels==0]).sum())/(labels[labels==0].size)\n","PA_rd = ((predictions[labels==1]==labels[labels==1]).sum())/(labels[labels==1].size)\n","\n","# CALCULATION OF USER ACCURACY FOR DEFORESTATION\n","a = CM[0][0]\n","b = CM[0][1]\n","ua_deforestation = a/(a+b)\n","c = CM[1][0]\n","d = CM[1][1]\n","ua_non_deforest = d/(c+d)\n","\n","# CALCULATION OF PRODUCER ACCURACY FOR DEFORESTATION\n","e = CM[0][0]\n","f = CM[1][0]\n","pa_deforestation = e/(e+f)\n","g = CM[0][1]\n","h = CM[1][1]\n","pa_non_deforest = h/(g+h)\n","\n","print(\"ACCURACY METRICS OF SEGNET RESULTS:\\n\")\n","print(\"Overall Accuracy: %f\" % OA)\n","print(\"F1 SCORE: %f\" %(f1_score(labels, predictions, average=\"macro\")))\n","print(\"PA of Deforestation: %f\" % PA_bldg)\n","print(\"PA of Non-Deforestation: %f\" % PA_rd)\n","\n","print(\"User Accuracy of Deforestation: %f\" % ua_deforestation)\n","print(\"User Accuracy of Non-Deforestation: %f\" % ua_non_deforest)\n","print(\"Producer Accuracy of Deforestation: %f\" % pa_deforestation)\n","print(\"Producer Accuracy of Non-Deforestation: %f\" % pa_non_deforest)\n","\n","mapfname = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/OUTPUT_TIFF/CLOUDY/2020/LATE_FUSION_FULL_IMAGE.tif\"\n","\n","predictions_ = predictions.flatten()\n","print_map(predictions_, mapidx, ccolors = ccolors, filename = mapfname)\n","fig = plt.figure(figsize=(15, 10))\n","ax = fig.add_subplot(1, 1, 1)\n","ax.imshow(predictions, cmap = cmap, norm = norm, interpolation = \"nearest\", origin = \"upper\")\n","ax.set_title(\"Predictions Using Late Fusion of Sentinel-1 and Sentinel-2\")\n","\n","# Code for Printing the Labels\n","rectangles = [matplotlib.patches.Rectangle((0, 0), 1, 1, color = norm_ccolors[r]) for r in range(norm_ccolors.shape[0])]\n","classes = [\"DEFORESTATION\", \"NON-DEFORESTATION\"]\n","ax.legend(rectangles, classes)\n","\n","plt.show()"]},{"cell_type":"code","source":["# PUTTING ALL THE TRUE AND FALSE VALUES INSIDE A \"CSV\" FILE\n","import pandas as pd\n","predictions_2 = predictions.ravel()\n","labels_2 = labels.ravel()\n","sentinel2_full = (predictions_2 == labels_2)\n","sentinel2_full_2 = pd.Series(sentinel2_full)\n","sentinel2_full_3 = pd.DataFrame(sentinel2_full_2)\n","sentinel2_full_4 = sentinel2_full_3.rename(columns = {0:'LATE_FUSION'})\n","sentinel2_full_4"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"n2cW1BdHcC-0","executionInfo":{"status":"ok","timestamp":1654294705525,"user_tz":-120,"elapsed":36,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"43550b4b-d13e-4a4e-e91a-a2079975c2b4"},"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           LATE_FUSION\n","0                 True\n","1                 True\n","2                 True\n","3                 True\n","4                 True\n","...                ...\n","126877691         True\n","126877692         True\n","126877693         True\n","126877694         True\n","126877695         True\n","\n","[126877696 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-a365636d-5d35-4838-9909-fb8dcf041830\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" 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Chhibber","userId":"00696279373011653662"}}},"execution_count":46,"outputs":[]},{"cell_type":"code","source":["# IMPORT THE ESSENTIAL LIBRARIES FOR PLOTTING TE CONFUSION MATRIX\n","import seaborn as sns\n","import matplotlib.pyplot as plt"],"metadata":{"id":"4lWP1SjN3pvd","executionInfo":{"status":"ok","timestamp":1654294938443,"user_tz":-120,"elapsed":240,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"execution_count":47,"outputs":[]},{"cell_type":"code","source":["# print(\"NORMALIZED CONFUSION MATRIX:\")\n","cm_normalized = np.round(CM/np.sum(CM, axis = 1).reshape(-1, 1), 2)\n","# print(cm_normalized)\n","sns.heatmap(cm_normalized, cmap = \"Greens\", annot = True, \n","            cbar_kws = {\"orientation\":\"vertical\", \"label\":\"Colour Bar\"},\n","            xticklabels = [\"Deforestation\", \"Non-Deforestation\"], yticklabels = [\"Deforestation\", \"Non-Deforestation\"], square = True)\n","sns.set(rc = {'figure.figsize':(8, 8)})\n","plt.xlabel(\"PREDICTIONS\")\n","plt.ylabel(\"GROUND TRUTH\")\n","plt.title(\"CONFUSION MATRIX\")\n","plt.show()"],"metadata":{"id":"s5c2wnQdkkeA","colab":{"base_uri":"https://localhost:8080/","height":574},"executionInfo":{"status":"ok","timestamp":1654294939838,"user_tz":-120,"elapsed":628,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"f6f4cd23-5ee1-4914-d027-510e02779984"},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"u_GDjpOebbDj"},"source":["# TEST SET 1"]},{"cell_type":"code","execution_count":49,"metadata":{"id":"Ir9dSRxLE5Hg","executionInfo":{"status":"ok","timestamp":1654294941665,"user_tz":-120,"elapsed":223,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# IMAGES TILES FROM FULL RASTER\n","x_test_S1_1 = image_tiles_S1_16_grid[\"_12\"]\n","x_test_S2_1 = image_tiles_S2_16_grid[\"_12\"]\n","y_test_1 = label_tiles_16_grid[\"_12\"]"]},{"cell_type":"code","execution_count":50,"metadata":{"id":"ITJ8RAlUqMmh","executionInfo":{"status":"ok","timestamp":1654294942355,"user_tz":-120,"elapsed":9,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# PUTTING THE TRANSFORMATION AND PROJECTION OF THE INPUT RASTER WITHIN VARIABLE FOR SENTINEL-1\n","ds_2 = gdal.Open(\"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_12.tif\")\n","gt_2 = ds_2.GetGeoTransform()\n","proj_2 = ds_2.GetProjection()"]},{"cell_type":"code","execution_count":51,"metadata":{"id":"k1-apIwaby-2","executionInfo":{"status":"ok","timestamp":1654294943667,"user_tz":-120,"elapsed":9,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["import string\n","from matplotlib import colors\n","from sklearn.metrics import f1_score, precision_score, recall_score\n","\n","# SET COLOUR CODES OF THE CLASSES\n","deforestation = [253, 231, 36]\n","non_deforestation = [32, 144, 140]\n","ccolors = (deforestation, non_deforestation)\n","NROWS, NCOLS, NBANDS2 = x_test_S2_1.shape\n","NROWS, NCOLS, NBANDS1 = x_test_S1_1.shape\n","\n","\"\"\"\n","    Print the classified map.\n","    Keyword arguments:\n","    predictions -- the array output labels from the classification\n","    results\n","    idxarray -- the array of indices of the classified samples\n","    to be plotted.\n","    filename -- the name (with the directory) of the file where to\n","    write the map.\n","    ccolors -- an array of tuples containing rgb values for the classes\n","    Both arrays should match and be arranged accordingly. And the\n","    index of the color should match the value of the of prediction.\n","\"\"\"\n","\n","def print_map_2(predictions, idxarray, filename = None, title = None, ccolors = None):\n","    if predictions.size != idxarray.shape[0]:\n","        raise ValueError(\"There should be the same number of \" +\n","                         \"predictions and indices.\")\n","    nrows, ncols = np.max(idxarray[:, 0])+1, np.max(idxarray[:, 1])+1\n","    classifiedmap = np.zeros((nrows, ncols, 3), dtype=\"uint8\")\n","    idx = 0\n","    for prediction in predictions:\n","        classifiedmap[idxarray[idx, 0],\n","                      idxarray[idx, 1]] = np.asarray(ccolors[prediction])\n","        idx += 1\n","    if filename is None:\n","        fig = plt.figure()\n","        plt.imshow(classifiedmap)\n","        if title:\n","            plt.suptitle(title)\n","        plt.show()\n","    else:\n","        output_raster = gdal.GetDriverByName('GTiff').Create(filename, NCOLS, NROWS, number_classes, gdal.GDT_Byte)\n","        output_raster.SetGeoTransform(gt_2)\n","        output_raster.SetProjection(proj_2)\n","        \n","        for i in range(number_classes):\n","            output_raster.GetRasterBand(i+1).WriteArray(classifiedmap.astype(np.uint8)[:, :, i])"]},{"cell_type":"code","execution_count":52,"metadata":{"id":"YD8PXRDkcJrP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654294986351,"user_tz":-120,"elapsed":41312,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"a610cfb4-8523-49ce-988d-34563bec39dd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Strip number: 0\n","Strip number: 1\n","Strip number: 2\n","Strip number: 3\n","Strip number: 4\n","Strip number: 5\n","Strip number: 6\n","Strip number: 7\n","Strip number: 8\n","Strip number: 9\n","Strip number: 10\n","Last hit!\n"]}],"source":["predictions_FCN = evaluate_predictions([x_test_S2_1, x_test_S1_1], weights_pretrained_FCN, cut, overlap, y_test_1, late_fusion_model)"]},{"cell_type":"code","execution_count":53,"metadata":{"id":"gKklkrzkcU9Q","colab":{"base_uri":"https://localhost:8080/","height":610},"executionInfo":{"status":"ok","timestamp":1654295016386,"user_tz":-120,"elapsed":1013,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"d04ce6a5-ce93-4c78-bdd8-aa38400f4cba"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# PLOT REFERENCE MAP\n","norm_ccolors = np.array(ccolors)/255.0\n","fig = plt.figure(figsize=(15, 10))\n","ax = fig.add_subplot(1, 1, 1)\n","cmap = colors.ListedColormap(norm_ccolors)\n","bounds = [0, 1, 2]\n","norm = colors.BoundaryNorm(bounds, cmap.N)\n","labels = y_test_1[:, :, 0].copy()\n","labels[labels!=2] = 0\n","labels[labels==2] = 1\n","ax.imshow(labels, cmap = cmap, norm=norm, interpolation = \"nearest\", origin = \"upper\")\n","ax.set_title(\"GROUND TRUTH\")\n","rectangles = [matplotlib.patches.Rectangle((0, 0), 1, 1, color=norm_ccolors[r]) for r in range(norm_ccolors.shape[0])]\n","classes = [\"DEFORESTATION\", \"NON-DEFORESTATION\"]\n","#Create legend from custom artist/label lists\n","ax.legend(rectangles, classes)\n","plt.show()\n","labels = labels.astype(\"uint8\")"]},{"cell_type":"code","execution_count":54,"metadata":{"id":"BOH6UT3acVes","colab":{"base_uri":"https://localhost:8080/","height":784},"executionInfo":{"status":"ok","timestamp":1654295033663,"user_tz":-120,"elapsed":17025,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"0667ed5b-e072-4f22-e8b7-b10167852935"},"outputs":[{"output_type":"stream","name":"stdout","text":["ACCURACY METRICS OF SEGNET RESULTS:\n","\n","Overall Accuracy: 0.909497\n","F1 SCORE: 0.905708\n","PA of Deforestation: 0.904186\n","PA of Non-Deforestation: 0.915512\n","User Accuracy of Deforestation: 0.904186\n","User Accuracy of Non-Deforestation: 0.915512\n","Producer Accuracy of Deforestation: 0.923788\n","Producer Accuracy of Non-Deforestation: 0.894025\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# EVALUATE SEGNET BASED LATE FUSION\n","predictions = predictions_FCN\n","mapidx = cartesian([range(predictions.shape[0]), range(predictions.shape[1])])\n","CM = get_confusion_matrix(predictions, labels)\n","OA = np.sum(np.diag(CM))/predictions.size\n","PA_bldg = ((predictions[labels==0]==labels[labels==0]).sum())/(labels[labels==0].size)\n","PA_rd = ((predictions[labels==1]==labels[labels==1]).sum())/(labels[labels==1].size)\n","\n","# CALCULATION OF USER ACCURACY FOR DEFORESTATION\n","a = CM[0][0]\n","b = CM[0][1]\n","ua_deforestation = a/(a+b)\n","c = CM[1][0]\n","d = CM[1][1]\n","ua_non_deforest = d/(c+d)\n","\n","# CALCULATION OF PRODUCER ACCURACY FOR DEFORESTATION\n","e = CM[0][0]\n","f = CM[1][0]\n","pa_deforestation = e/(e+f)\n","g = CM[0][1]\n","h = CM[1][1]\n","pa_non_deforest = h/(g+h)\n","\n","print(\"ACCURACY METRICS OF SEGNET RESULTS:\\n\")\n","print(\"Overall Accuracy: %f\" % OA)\n","print(\"F1 SCORE: %f\" %(f1_score(labels, predictions, average=\"macro\")))\n","print(\"PA of Deforestation: %f\" % PA_bldg)\n","print(\"PA of Non-Deforestation: %f\" % PA_rd)\n","\n","print(\"User Accuracy of Deforestation: %f\" % ua_deforestation)\n","print(\"User Accuracy of Non-Deforestation: %f\" % ua_non_deforest)\n","print(\"Producer Accuracy of Deforestation: %f\" % pa_deforestation)\n","print(\"Producer Accuracy of Non-Deforestation: %f\" % pa_non_deforest)\n","\n","mapfname = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/OUTPUT_TIFF/CLOUDY/2020/LATE_FUSION_TEST1.tif\"\n","\n","predictions_ = predictions.flatten()\n","print_map_2(predictions_, mapidx, ccolors = ccolors, filename = mapfname)\n","fig = plt.figure(figsize=(15, 10))\n","ax = fig.add_subplot(1, 1, 1)\n","ax.imshow(predictions, cmap = cmap, norm = norm, interpolation = \"nearest\", origin = \"upper\")\n","ax.set_title(\"Predictions Using Late Fusion of Sentinel-1 and Sentinel-2\")\n","\n","# Code for Printing the Labels\n","rectangles = [matplotlib.patches.Rectangle((0, 0), 1, 1, color = norm_ccolors[r]) for r in range(norm_ccolors.shape[0])]\n","classes = [\"DEFORESTATION\", \"NON-DEFORESTATION\"]\n","ax.legend(rectangles, classes)\n","\n","plt.show()"]},{"cell_type":"code","source":["# PUTTING ALL THE TRUE AND FALSE VALUES INSIDE A \"CSV\" FILE\n","import pandas as pd\n","predictions_2 = predictions.ravel()\n","labels_2 = labels.ravel()\n","sentinel2_full = (predictions_2 == labels_2)\n","sentinel2_full_2 = pd.Series(sentinel2_full)\n","sentinel2_full_3 = pd.DataFrame(sentinel2_full_2)\n","sentinel2_full_4 = sentinel2_full_3.rename(columns = {0:'LATE_FUSION'})\n","sentinel2_full_4"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"Txa1kLk4cOhk","executionInfo":{"status":"ok","timestamp":1654295043254,"user_tz":-120,"elapsed":211,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"bb0c3600-ce81-4340-b212-46ead40dc2e5"},"execution_count":55,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         LATE_FUSION\n","0              False\n","1              False\n","2              False\n","3              False\n","4              False\n","...              ...\n","7929851         True\n","7929852         True\n","7929853         True\n","7929854         True\n","7929855         True\n","\n","[7929856 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-57296ce5-1ef2-449b-8a45-73b0b6f008ec\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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Chhibber","userId":"00696279373011653662"}}},"execution_count":56,"outputs":[]},{"cell_type":"code","source":["# print(\"NORMALIZED CONFUSION MATRIX:\")\n","cm_normalized = np.round(CM/np.sum(CM, axis = 1).reshape(-1, 1), 2)\n","# print(cm_normalized)\n","sns.heatmap(cm_normalized, cmap = \"Greens\", annot = True, \n","            cbar_kws = {\"orientation\":\"vertical\", \"label\":\"Colour Bar\"},\n","            xticklabels = [\"Deforestation\", \"Non-Deforestation\"], yticklabels = [\"Deforestation\", \"Non-Deforestation\"], square = True)\n","sns.set(rc = {'figure.figsize':(8, 8)})\n","plt.xlabel(\"PREDICTIONS\")\n","plt.ylabel(\"GROUND TRUTH\")\n","plt.title(\"CONFUSION MATRIX\")\n","plt.show()"],"metadata":{"id":"iXaCqt2b4Ei2","colab":{"base_uri":"https://localhost:8080/","height":466},"executionInfo":{"status":"ok","timestamp":1654295060477,"user_tz":-120,"elapsed":1196,"user":{"displayName":"Akhil 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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"_PZcFnyGcbUP"},"source":["# TEST SET 2"]},{"cell_type":"code","execution_count":58,"metadata":{"id":"fdvSatOEcl9s","executionInfo":{"status":"ok","timestamp":1654295060759,"user_tz":-120,"elapsed":13,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# IMAGES TILES FROM FULL RASTER\n","x_test_S1_2 = image_tiles_S1_16_grid[\"_14\"]\n","x_test_S2_2 = image_tiles_S2_16_grid[\"_14\"]\n","y_test_2 = label_tiles_16_grid[\"_14\"]"]},{"cell_type":"code","execution_count":59,"metadata":{"id":"3gOj6dEwqZPe","executionInfo":{"status":"ok","timestamp":1654295061610,"user_tz":-120,"elapsed":10,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["# PUTTING THE TRANSFORMATION AND PROJECTION OF THE INPUT RASTER WITHIN VARIABLE FOR SENTINEL-1\n","ds_3 = gdal.Open(\"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/CLOUDY_DATASET/SENTINEL2/2020/20201025/IMAGE_16_GRID/RASTER_14.tif\")\n","gt_3 = ds_3.GetGeoTransform()\n","proj_3 = ds_3.GetProjection()"]},{"cell_type":"code","execution_count":60,"metadata":{"id":"wlHXpWGScl6k","executionInfo":{"status":"ok","timestamp":1654295062952,"user_tz":-120,"elapsed":11,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}}},"outputs":[],"source":["import string\n","from matplotlib import colors\n","from sklearn.metrics import f1_score, precision_score, recall_score\n","\n","# SET COLOUR CODES OF THE CLASSES\n","deforestation = [253, 231, 36]\n","non_deforestation = [32, 144, 140]\n","ccolors = (deforestation, non_deforestation)\n","NROWS, NCOLS, NBANDS2 = x_test_S2_2.shape\n","NROWS, NCOLS, NBANDS1 = x_test_S1_2.shape\n","\n","\"\"\"\n","    Print the classified map.\n","    Keyword arguments:\n","    predictions -- the array output labels from the classification\n","    results\n","    idxarray -- the array of indices of the classified samples\n","    to be plotted.\n","    filename -- the name (with the directory) of the file where to\n","    write the map.\n","    ccolors -- an array of tuples containing rgb values for the classes\n","    Both arrays should match and be arranged accordingly. And the\n","    index of the color should match the value of the of prediction.\n","\"\"\"\n","\n","def print_map_3(predictions, idxarray, filename = None, title = None, ccolors = None):\n","    if predictions.size != idxarray.shape[0]:\n","        raise ValueError(\"There should be the same number of \" +\n","                         \"predictions and indices.\")\n","    nrows, ncols = np.max(idxarray[:, 0])+1, np.max(idxarray[:, 1])+1\n","    classifiedmap = np.zeros((nrows, ncols, 3), dtype=\"uint8\")\n","    idx = 0\n","    for prediction in predictions:\n","        classifiedmap[idxarray[idx, 0],\n","                      idxarray[idx, 1]] = np.asarray(ccolors[prediction])\n","        idx += 1\n","    if filename is None:\n","        fig = plt.figure()\n","        plt.imshow(classifiedmap)\n","        if title:\n","            plt.suptitle(title)\n","        plt.show()\n","    else:\n","        output_raster = gdal.GetDriverByName('GTiff').Create(filename, NCOLS, NROWS, number_classes, gdal.GDT_Byte)\n","        output_raster.SetGeoTransform(gt_3)\n","        output_raster.SetProjection(proj_3)\n","\n","        for i in range(number_classes):\n","            output_raster.GetRasterBand(i+1).WriteArray(classifiedmap.astype(np.uint8)[:, :, i])"]},{"cell_type":"code","execution_count":61,"metadata":{"id":"oVDuiqX8cl4M","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654295105331,"user_tz":-120,"elapsed":38667,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"d9026a49-499f-431e-c995-785d937d4276"},"outputs":[{"output_type":"stream","name":"stdout","text":["Strip number: 0\n","Strip number: 1\n","Strip number: 2\n","Strip number: 3\n","Strip number: 4\n","Strip number: 5\n","Strip number: 6\n","Strip number: 7\n","Strip number: 8\n","Strip number: 9\n","Strip number: 10\n","Last hit!\n"]}],"source":["predictions_FCN = evaluate_predictions([x_test_S2_2, x_test_S1_2], weights_pretrained_FCN, cut, overlap, y_test_2, late_fusion_model)"]},{"cell_type":"code","execution_count":62,"metadata":{"id":"SwLdup-jcl10","colab":{"base_uri":"https://localhost:8080/","height":610},"executionInfo":{"status":"ok","timestamp":1654295106008,"user_tz":-120,"elapsed":693,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"414850de-74e9-4cc2-bc8d-34762240afe9"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# PLOT REFERENCE MAP\n","norm_ccolors = np.array(ccolors)/255.0\n","fig = plt.figure(figsize=(15, 10))\n","ax = fig.add_subplot(1, 1, 1)\n","cmap = colors.ListedColormap(norm_ccolors)\n","bounds = [0,1,2]\n","norm = colors.BoundaryNorm(bounds, cmap.N)\n","labels = y_test_2[:, :, 0].copy()\n","labels[labels!=2] = 0\n","labels[labels==2] = 1\n","ax.imshow(labels, cmap = cmap, norm=norm, interpolation = \"nearest\", origin = \"upper\")\n","ax.set_title(\"GROUND TRUTH\")\n","rectangles = [matplotlib.patches.Rectangle((0, 0), 1, 1, color=norm_ccolors[r]) for r in range(norm_ccolors.shape[0])]\n","classes = [\"DEFORESTATION\", \"NON-DEFORESTATION\"]\n","#Create legend from custom artist/label lists\n","ax.legend(rectangles, classes)\n","plt.show()\n","labels = labels.astype(\"uint8\")"]},{"cell_type":"code","execution_count":63,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":784},"executionInfo":{"elapsed":16753,"status":"ok","timestamp":1654295132493,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"},"user_tz":-120},"id":"a2a0m1cXclzy","outputId":"469bb887-1896-42ee-a7e1-96a9bb89cd20"},"outputs":[{"output_type":"stream","name":"stdout","text":["ACCURACY METRICS OF SEGNET RESULTS:\n","\n","Overall Accuracy: 0.884135\n","F1 SCORE: 0.791810\n","PA of Deforestation: 0.926850\n","PA of Non-Deforestation: 0.794363\n","User Accuracy of Deforestation: 0.926850\n","User Accuracy of Non-Deforestation: 0.794363\n","Producer Accuracy of Deforestation: 0.904514\n","Producer Accuracy of Non-Deforestation: 0.837845\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# EVALUATE SEGNET BASED LATE FUSION\n","predictions = predictions_FCN\n","mapidx = cartesian([range(predictions.shape[0]), range(predictions.shape[1])])\n","CM = get_confusion_matrix(predictions, labels)\n","OA = np.sum(np.diag(CM))/predictions.size\n","PA_bldg = ((predictions[labels==0]==labels[labels==0]).sum())/(labels[labels==0].size)\n","PA_rd = ((predictions[labels==1]==labels[labels==1]).sum())/(labels[labels==1].size)\n","\n","# CALCULATION OF USER ACCURACY FOR DEFORESTATION\n","a = CM[0][0]\n","b = CM[0][1]\n","ua_deforestation = a/(a+b)\n","c = CM[1][0]\n","d = CM[1][1]\n","ua_non_deforest = d/(c+d)\n","\n","# CALCULATION OF PRODUCER ACCURACY FOR DEFORESTATION\n","e = CM[0][0]\n","f = CM[1][0]\n","pa_deforestation = e/(e+f)\n","g = CM[0][1]\n","h = CM[1][1]\n","pa_non_deforest = h/(g+h)\n","\n","print(\"ACCURACY METRICS OF SEGNET RESULTS:\\n\")\n","print(\"Overall Accuracy: %f\" % OA)\n","print(\"F1 SCORE: %f\" %(f1_score(labels, predictions, average=\"macro\")))\n","print(\"PA of Deforestation: %f\" % PA_bldg)\n","print(\"PA of Non-Deforestation: %f\" % PA_rd)\n","\n","print(\"User Accuracy of Deforestation: %f\" % ua_deforestation)\n","print(\"User Accuracy of Non-Deforestation: %f\" % ua_non_deforest)\n","print(\"Producer Accuracy of Deforestation: %f\" % pa_deforestation)\n","print(\"Producer Accuracy of Non-Deforestation: %f\" % pa_non_deforest)\n","\n","mapfname = \"/content/drive/MyDrive/MASTER_RESEARCH/MODEL_TESTING/2_DEFORESTATION/AKHIL_MODEL/OUTPUT_TIFF/CLOUDY/2020/LATE_FUSION_TEST2.tif\"\n","\n","predictions_ = predictions.flatten()\n","print_map_3(predictions_, mapidx, ccolors = ccolors, filename = mapfname)\n","fig = plt.figure(figsize=(15, 10))\n","ax = fig.add_subplot(1, 1, 1)\n","ax.imshow(predictions, cmap = cmap, norm = norm, interpolation = \"nearest\", origin = \"upper\")\n","ax.set_title(\"Predictions Using Level Fusion of Sentinel-1 and Sentinel-2\")\n","\n","# Code for Printing the Labels\n","rectangles = [matplotlib.patches.Rectangle((0, 0), 1, 1, color = norm_ccolors[r]) for r in range(norm_ccolors.shape[0])]\n","classes = [\"DEFORESTATION\", \"NON-DEFORESTATION\"]\n","ax.legend(rectangles, classes)\n","\n","plt.show()"]},{"cell_type":"code","source":["# PUTTING ALL THE TRUE AND FALSE VALUES INSIDE A \"CSV\" FILE\n","import pandas as pd\n","predictions_2 = predictions.ravel()\n","labels_2 = labels.ravel()\n","sentinel2_full = (predictions_2 == labels_2)\n","sentinel2_full_2 = pd.Series(sentinel2_full)\n","sentinel2_full_3 = pd.DataFrame(sentinel2_full_2)\n","sentinel2_full_4 = sentinel2_full_3.rename(columns = {0:'LATE_FUSION'})\n","sentinel2_full_4"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"39fKYyHscbLS","executionInfo":{"status":"ok","timestamp":1654295136075,"user_tz":-120,"elapsed":471,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"e43bc002-24f7-4d73-b063-51b54bedef69"},"execution_count":64,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         LATE_FUSION\n","0               True\n","1               True\n","2               True\n","3               True\n","4               True\n","...              ...\n","7929851         True\n","7929852         True\n","7929853         True\n","7929854         True\n","7929855         True\n","\n","[7929856 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-45b15378-c812-40fb-8405-e76a49ff9317\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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Chhibber","userId":"00696279373011653662"}}},"execution_count":65,"outputs":[]},{"cell_type":"code","execution_count":66,"metadata":{"id":"cQ3jLMAWerQl","colab":{"base_uri":"https://localhost:8080/","height":466},"executionInfo":{"status":"ok","timestamp":1654295153635,"user_tz":-120,"elapsed":533,"user":{"displayName":"Akhil Chhibber","userId":"00696279373011653662"}},"outputId":"e24acb93-7c6b-4025-f571-ba8f3b459cde"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 2 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\n"},"metadata":{}}],"source":["# print(\"NORMALIZED CONFUSION MATRIX:\")\n","cm_normalized = np.round(CM/np.sum(CM, axis = 1).reshape(-1, 1), 2)\n","# print(cm_normalized)\n","sns.heatmap(cm_normalized, cmap = \"Greens\", annot = True, \n","            cbar_kws = {\"orientation\":\"vertical\", \"label\":\"Colour Bar\"},\n","            xticklabels = [\"Deforestation\", \"Non-Deforestation\"], yticklabels = [\"Deforestation\", \"Non-Deforestation\"], square = True)\n","sns.set(rc = {'figure.figsize':(8, 8)})\n","plt.xlabel(\"PREDICTIONS\")\n","plt.ylabel(\"GROUND TRUTH\")\n","plt.title(\"CONFUSION MATRIX\")\n","plt.show()"]},{"cell_type":"code","source":[""],"metadata":{"id":"rfHJxRni4XzW"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"machine_shape":"hm","name":"53_LATE_FUSION_SEGNET_2020.ipynb","provenance":[],"background_execution":"on","authorship_tag":"ABX9TyPuUXfnYSGihAjkpNhUzVks"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}