@inproceedings{dd6a78ac4a4e4efcaad82a18bb2e3f24,
title = "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.",
keywords = "Maximum a Posterior, fast iterative shrinkage thresholding algorithm, gradient descent, split Bregman algorithm, subpixel mapping, total variation",
author = "Zhongkai Hu and Kun Gao and Zeyang Dou",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Workshop on Remote Sensing with Intelligent Processing, RSIP 2017 ; Conference date: 19-05-2017 Through 21-05-2017",
year = "2017",
month = jun,
day = "23",
doi = "10.1109/RSIP.2017.7958809",
language = "English",
series = "RSIP 2017 - International Workshop on Remote Sensing with Intelligent Processing, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "RSIP 2017 - International Workshop on Remote Sensing with Intelligent Processing, Proceedings",
address = "United States",
}