NONLOCAL SIMILARITY REGULARIZATION FOR SPARSE HYPERSPECTRAL UNMIXING

被引：5

作者：

Wang, Rui ^{[1
]}

Li, Heng-Chao ^{[1
]}

机构：

[1] Southwest Jiaotong Univ, Sichuan Prov Key Lab Informat Coding & Transmiss, Chengdu 610031, Peoples R China

来源：

2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2014年

关键词：

Hyperspectral remote sensing; sparse unmixing; nonlocal similarity regularization; spectral library;

D O I：

10.1109/IGARSS.2014.6947089

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 is concerned with semisupervised hyperspectral unmixing using a nonlocal similarity prior on the abundance images. To this end, the nonlocal self-similarity regularization is incorporated into the classical sparse regression formula to propose a new model for hyperspectral sparse unmixing. The rationale is the idea that there are many nonlocal similar patches to the given patch in the abundance images. The effectiveness of the proposed algorithm is illustrated using the synthetic and real data sets.

引用

页数：4

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