Spectral-spatial hyperspectral classification via shape-adaptive sparse representation

This paper proposes a new spectral-spatial hyperspectral classification method named the shape-adaptive sparse representation (SASR). The fixed window is not suitable for all pixels of hyperspectral image (HSI) to search local similar regions. In order to overcome the drawback, we propose to apply the shape-adaptive algorithm to exploit the contextual spatial information of HSI. Furthermore, the hyperspectral classification is implemented by incorporating the spatial contextual information of HSI into the sparse representation classification model. Experimental results demonstrate the superiority of the proposed SASR method over both classical and state-of-the-art approaches.