A fast hyperspectral subpixel mapping algorithm based on MAP-TV framework

Abstract

The subpixel mapping technique can obtain a fine-resolution map of target classes in the hyperspectral remote sensing image based on the spatial dependence. In recent years, the subpixel mapping methods based on Maximum A Posterior framework and Total Variation prior (MAP-TV) has received extensive attention because of its unified framework. However, due to the inherent nonlinearity of the TV prior, the traditional gradient descent algorithm to minimize MAP-TV model is inefficient. In this paper, we propose a fast algorithm to solve the MAP-TV model, which combined the fast iterative shrinkage thresholding algorithm and split Bregman algorithm together. The proposed algorithm split the original problem into several sub-problems, each sub-problem has the closed-form solution and is fast to compute. The numerical experiments reveal that the proposed algorithm is faster than the traditional methods and is suitable for the hyperspectral subpixel mapping applications.