Multi-task jointly sparse spectral unmixing method based on spectral similarity measure of hyperspectral imagery

被引：0
|
作者：
Xu N. [1 ,2 ,3 ]
You H. [1 ,2 ]
Geng X. [1 ,2 ]
Cao Y. [4 ]
机构：
[1] Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing
[2] Institute of Electronics, Chinese Academy of Sciences, Beijing
[3] University of Chinese Academy of Sciences, Beijing
[4] School of Land Science and Technology, China University of Geosciences, Beijing
来源：
Xu, Ning (x_ning@aliyun.com) | 1600年 / Science Press卷 / 38期
关键词：
Hyperspectral imagery; Joint sparse representation; Spectral similarity measure; Spectral unmixing;
D O I：
10.11999/JEIT160011
中图分类号：
学科分类号：
摘要：
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. © 2016, Science Press. All right reserved.
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页码：2701 / 2708
页数：7
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