Hyperspectral Image Classification by Spatial–Spectral Derivative-Aided Kernel Joint Sparse Representation

Sparse representation exhibits good performance in various image processing and has been applied to hyperspectral image (HSI) classification by many researchers. Recently, several new spatial-spectral strategies combined with sparse representation have been proposed to improve classification performance. However, these new strategies rely on spectral reflectance information and its neighborhood, without considering other spectral properties and higher order context information. Thus, in this paper, we present a spatial-spectral derivative-aided kernel joint sparse representation (KJSR-SSDK) for HSI classification. The proposed algorithm includes three novelties: 1) it considers the derivative features of the spectral as well as the original spectral feature; 2) it incorporates higher order spatial context and distinct spectral information; and 3) the l_1,2 mix-norm regularization is imposed on the coefficients of spatial-spectral derivative-aided dictionary for KJSR. Based on the rich experimental comparison with the related state-of-the-art algorithms, the effectiveness of the proposed KJSR-SSDK has been confirmed.