Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery

XU Ning; YOU Hongjian; GENG Xiurui; CAO Yingui

doi:10.11999/JEIT160011

Volume 38 Issue 11

Dec. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(11): 2701-2708

XU Ning, YOU Hongjian, GENG Xiurui, CAO Yingui. Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2701-2708. doi: 10.11999/JEIT160011

Citation:

XU Ning, YOU Hongjian, GENG Xiurui, CAO Yingui. Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2701-2708. doi: 10.11999/JEIT160011

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Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery

doi: 10.11999/JEIT160011 cstr: 32379.14.JEIT160011

Funds:

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)

Received Date: 2016-01-04
Rev Recd Date: 2016-06-06
Publish Date: 2016-11-19

Abstract

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.
- Hyperspectral imagery,
- Spectral unmixing,
- Joint sparse representation,
- Spectral similarity measure

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References(33)

References

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