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With regard to the specific role of each pixel within a spatial parcel of a hyperspectral image (HSI), we propose a novel superpixel-oriented sparse representation classification method with a multi-task learning approach. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and also the correlation and distinctiveness of pixels in a spatial local region. Compared...
A column generation kernel technology based nonlinear regression classification method for hyperspectral image is proposed in this paper. The nonlinear extension for the collaborative representation regression is utilized in the joint collaboration model framework. The proposed algorithm is tested on two hyperspectral images. Experimental results suggest that the proposed nonlinear algorithm shows...
A nonlocal weighted joint sparse representation classification method for hyperspectral image is proposed in this paper. A discriminated contributions based on nonlocal spatial structure information are utilized in the joint sparsity model framework. The proposed algorithm is tested on two hyperspectral images. Experimental results suggest that the proposed algorithm shows superior performance over...
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