RANDOM-PROJECTION-BASED NONNEGATIVE LEAST SQUARES FOR HYPERSPECTRAL IMAGE UNMIXING

被引：0

作者：

Menon, Vineetha ^{[1
]}

Du, Qian ^{[1
]}

Fowler, James E. ^{[1
]}

机构：

[1] Mississippi State Univ, Dept Elect & Comp Engn, Geosyst Res Inst, Mississippi State, MS 39762 USA

来源：

2016 8TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS) | 2016年

关键词：

random projection; nonnegative least squares; hyperspectral unmixing; MATRIX FACTORIZATION; REDUCTION;

D O I：

暂无

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

Nonnegative least squares, a state-of-the-art approach to end-member abundance estimation in the hyperspectral-unmixing problem, is coupled with random projection employed for dimensionality reduction. Both Hadamard- and Gaussian-based projections are considered. Experimental results reveal that random projections can significantly reduce data volume without detrimentally affecting the accuracy of the abundance estimation, with the Hadamard-based approach slightly outperforming its Gaussian counterpart.

引用

页数：5

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