Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery
XU Ning①②③ YOU Hongjian①② GENG Xiurui①② CAO Yingui④
①(Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China) ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China) ③(University of Chinese Academy of Sciences, Beijing 100049, China) ④(School of Land Science and Technology, China University of Geosciences, Beijing 100083, China)
In this paper, a multi-task jointly sparse spectral unmixing method based on spectral similarity measure of hyperspectral imagery is proposed, which is a refinement of collaborative sparse spectral unmixing method. First, a threshold value is obtained through the statistical characters of some random selected neighboring pixels in hypersepctral image. Second, all pixels of hyperspectral image are grouped by a spectral similarity measure and the threshold value. Then, a multi-task jointly sparse optimization problem is constructed and solved for the grouped pixels, and the abundance coefficients are obtained finally. Experimentals results on synthetic and real hyperspectral image demonstrate the effectiveness of the proposed approach.
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