GLUP: YET ANOTHER ALGORITHM FOR BLIND UNMIXING OF HYPERSPECTRAL DATA

被引：0

作者：

Ammanouil, Rita ^{[1
]}

Ferrari, Andre ^{[1
]}

Richard, Cedric ^{[1
]}

Mary, David ^{[1
]}

机构：

[1] Univ Nice Sophia Antipolis, CNRS, Observ Cote Azur, Nice, France

来源：

2014 6TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS) | 2014年

关键词：

Hyperspectral data; blind unmixing; linear mixing model; ADMM; MODEL;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper addresses the problem of blind fully-constrained linear unmixing of hyperspectral images. The endmembers and their cardinality are assumed unknown, but the endmember spectra are supposed to be present in the scene. Group Lasso regularization is used to extract the endmembers. The estimation problem is convex, and solved with the Alternating Direction Method of Multipliers. Comparisons with state-of-the-art methods on synthetic and real data sets show the efficiency of our approach.

引用

页数：4

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