基于光谱相似度量的高光谱图像多任务联合稀疏光谱解混方法

doi:10.11999/JEIT160011

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Abstract

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.

Key words： Hyperspectral imagery Spectral unmixing Joint sparse representation Spectral similarity measure

Received: 04 January 2016 Published: 01 September 2016

PACS:

TP751

Fund:

The Geological Survey Program of China Geological Survey (1212011120226), The National 863 Program of China (2012AA12A308), The Science and Technology Services Network Program of Chinese Academy of Sciences (KFJ-EW- STS-046)

Corresponding Authors: XU Ning E-mail: x_ning@aliyun.com

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	XU Ning
	YOU Hongjian
	GENG Xiurui
	CAO Yingui

Cite this article:

XU Ning,YOU Hongjian,GENG Xiurui等. Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery[J]. JEIT, 2016, 38(11): 2701-2708.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT160011 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2701

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