Demo for RCRD detection algorithm
1. Brief abstract
This paper proposes a new relaxed collaborative representation detector for hyperspectral anomaly detection by using a novel non-global dictionary. The proposed detector conducts collaborative representation on each feature dimension of the pixel under test and simultaneously constrains the coding vectors of different features to be similar. To the best of our knowledge, this is the first time that a detection model is built from each feature dimension. To adjust the contributions of each feature, an adaptive feature weight constrained version of the method is also proposed. The non-global dictionary is constructed by combining the k-nearest neighbor method and an existing global dictionary, which is more reliable and practical than the widely used dual windows dictionary.
2. The flowchart of RCRD
3. More details
Z. Wu, H. Su, X. Tao, L. Han, M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, Hyperspectral Anomaly Detection With Relaxed Collaborative Representation, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022.
Please feel free to use the code or improve it. If you find this code or paper helpful to your research, please kindly cite the paper. Thank you!