Extended Collaborative Representation-Based Hyperspectral Imagery Classification

Abstract

Collaborative representation (CR) has been demonstrated to be very effective for hyperspectral image classification. However, insufficient diversity of training samples often results in limited classification accuracy under small-training-sample conditions, especially when diverse spectral variation is presented in testing samples. In order to alleviate such a problem, a spectral variation augmented-based linear mixed model (SV-LMM) is proposed, in which the spectral variation is extracted by conducting singular value decomposition (SVD) over training samples. Such spectral variation is further utilized to extend the CR for hyperspectral classification. Experiments over two benchmark datasets, i.e., the Pavia Center dataset and the University of Houston dataset, demonstrate that the proposed extended CR-based classifier (ECRC) clearly improves the performance of conventional CRC for hyperspectral classification and outperforms several state-of-the-art algorithms.