This repository provides supplementary materials for the paper entitled 'Remote sensing framework for lithological mapping via stacked autoencoders and clustering'. This paper proposes a framework based on different dimensionality reduction methods, including principal component analysis, canonical autoencoders, stacked autoencoders, and the k-means clustering algorithm to generate clustered maps using multispectral remote sensing data which are interpreted as lithological maps. This framework is applied to three different data types, including Landsat 8, ASTER, and Sentinel-2, and the results can be found in the notebooks called 'Autoencoder_Landsat8', 'Autoencoder_ASTER', and 'Autoencoder_Sentinel2'.
Nagar, S., Farahbakhsh, E., Awange, J., Chandra, R., Remote sensing framework for lithological mapping via stacked autoencoders and clustering [Under Review]