Hyperspectral Imagery Classification With Multiple Regularized Collaborative Representations

Recent advances have shown a great potential to explore collaborative representations in hyperspectral imagery (HSI) classification, including sparse representations and joint collaborative representations. In this letter, we propose a weighted regularized collaborative representation optimized classifier (WRCROC) that makes use of multiple collaborative representations. It strikes a balance between an optimized weighted joint collaborative representation classifier, which essentially classifies a test sample to the class that minimizes the distance between the sample and its representation in the selected class, and a weighted regularized collaborative representation classifier, which actually assigns a test sample to the class that minimizes the distance between the sample and its collaborative components. The proposed WRCROC algorithm is tested on two benchmark HSI data sets. Experimental results demonstrate that the proposed algorithm performs better than existing representation-based classifiers.