Efficient superpixel-oriented multi-task joint sparse representation classification for hyperspectral imagery

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 with the state-of-the-art hyperspectral classifiers, the superiority of the spatial prior utilization, the multiple-feature fusion, and the computational efficiency are maintained at the same time in the proposed method. The proposed classification framework was tested on two HSIs. The experimental results suggest that the proposed algorithm performs better than the other representation-based classification algorithms and some popular hyperspectral multiple-feature classifiers.