SMOOTH SPECTRAL UNMIXING USING TOTAL VARIATION REGULARIZATION AND A FIRST ORDER ROUGHNESS PENALTY

被引：7

作者：

Sigurdsson, Jakob ^{[1
]}

Ulfarsson, Magnus O. ^{[1
]}

Sveinsson, Johannes R. ^{[1
]}

Benediktsson, Jon Atli ^{[1
]}

机构：

[1] Univ Iceland, Dept Elect Engn, Reykjavik, Iceland

来源：

2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2013年

关键词：

Spectral unmixing; blind signal separation; linear unmixing; total variation; roughness penalty; cyclic descent; majorization-minimization; ALGORITHM;

D O I：

10.1109/IGARSS.2013.6723242

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：

Hyperspectral unmixing is the task of decomposing hyper-spectral images into endmembers and their abundances. The endmembers are spectral signatures of specific material in the image and the abundances dictate the amount of the material found in each pixel. In this paper we present a blind signal separation method, based on the total variation penalty, that simultaneously estimates the endmembers and the abundances. We evaluate our method using both simulated and a real data set.

引用

页码：2160 / 2163

页数：4

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