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《Transactions of Beijing Institute of Technology》 2019-08
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A Fast Method for Hyperspectral Image Subpixel Mapping Based on Maximum a Posteriori and Total Variation Estimation

HU Zhong-kai;GAO Kun;DOU Ze-yang;ZHOU Ying-jie;GONG Xue-mei;School of Optics and Photonics, Beijing Institute of Technology;  
To solve the ill-posed problem of spectral unmixing in hyperspectral subpixel mapping applications, the maximum a posteriori estimation(MAP) spectral unmixing model combined with spatial distribution prior total variation(TV) was improved to ensure the scalability of the algorithm and the uniqueness of the solution. At the same time, in order to solve the cumbersome problem caused by the inherent nonlinear characteristics of TV prior, a fast algorithm was proposed to transform the original complex nonlinear operation into several simple operations with closed solutions. To solve the sub-problem respectively, a fast iterative shrinkage threshold algorithm(FISTA) and the split Bregman algorithm were utilized. The results show that the proposed new method can maintain the consistent mapping accuracy of the traditional gradient descent method, and can increase the iteration speed by more than 10 times, providing higher computational efficiency.
【Fund】: 国家自然科学基金资助项目(61875013 61827814);; 国家科技部重大科学仪器设备开发专项资助项目(2017YFF0107102)
【CateGory Index】: TP751
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