Weighted Kernel joint sparse representation for hyperspectral image classification

Kernel joint sparse representation (KJSR) performs joint sparse representation in the feature space and has shown good performance for the hyperspectral image (HSI) classification. In order to distinguish spatial neighbouring pixels in the feature space, we propose two weighted KJSR (WKJSR) methods in this paper. The first one computes the weight directly based on the kernel similarity between neighbouring pixels. The second weighted scheme uses a nearest regularisation strategy to simultaneously optimise the weights of projected neighbouring pixels and joint sparse representation coefficients. The proposed WKJSR methods can exploit the similarities and differences among neighbouring pixels to obtain accurate weights for the joint sparse representation and classification. Experimental results on two benchmark HSI data sets demonstrate the effectiveness of the proposed methods.