UM
Reweighted Sparse Regression for Hyperspectral Unmixing
Zheng C.Y.1; Li H.1; Wang Q.1; Philip Chen C.L.2
2016
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN01962892
Volume54Issue:1Pages:479-488
AbstractHyperspectral unmixing (HSU) plays an important role in hyperspectral image (HSI) analysis. Recently, the HSU method based on sparse regression has drawn much attention. This paper presents a new weighted sparse regression problem for HSU and proposes two iterative reweighted algorithms for solving this problem, where the weights used for the next iteration are computed from the value of the current solution, and all the mixed pixels of an HSI are unmixed simultaneously. The proposed algorithms can be seen as the combinations of alternating direction method of multipliers and iterative reweighting procedure. Experimental results on both synthetic and real data demonstrate some advantages of the proposed algorithms over some other state-of-the-art sparse unmixing approaches.
KeywordHyperspectral unmixing (HSU) iterative reweighting sparse regression
DOI10.1109/TGRS.2015.2459763
URLView the original
Language英語
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Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Huazhong University of Science and Technology
2.Universidade de Macau
3.Wuyi University
Recommended Citation
GB/T 7714
Zheng C.Y.,Li H.,Wang Q.,et al. Reweighted Sparse Regression for Hyperspectral Unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing,2016,54(1):479-488.
APA Zheng C.Y.,Li H.,Wang Q.,&Philip Chen C.L..(2016).Reweighted Sparse Regression for Hyperspectral Unmixing.IEEE Transactions on Geoscience and Remote Sensing,54(1),479-488.
MLA Zheng C.Y.,et al."Reweighted Sparse Regression for Hyperspectral Unmixing".IEEE Transactions on Geoscience and Remote Sensing 54.1(2016):479-488.
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