A hyperspectral spatial-spectral enhancement algorithm

Low spatial and spectral resolution hyperspectral image will always degrade the performance of the subsequent applications, such as classification and object detection. The desired hyperspectral image is assumed to be reconstructed based on both high spatial and spectral features, which are always represented using endmembers and their abundances. In this paper, we propose a hyperspectral spatial and spectral resolution enhancement algorithm based on spectral unmixing and spatial constraints to simultaneously obtain high spatial-spectral resolution result. An intermediate high spatial but low spectral resolution HSI is introduced to establish mapping scheme of abundances and endmembers between low resolution input and desired high spatial-spectral resolution result. Experiments on the Sandigo dataset have illustrated that the proposed method is comparable or superior to other state-of-art methods.