WAVELET BASED SPARSE PRINCIPAL COMPONENT ANALYSIS FOR HYPERSPECTRAL DENOISING

被引：0

作者：

Rasti, Behnood ^{[1
]}

Sveinsson, Johannes R. ^{[1
]}

Ulfarsson, Magnus O. ^{[1
]}

Sigurdsson, Jakob ^{[1
]}

机构：

[1] Univ Iceland, Fac Elect & Comp Engn, Hjardarhagi 2-6, IS-107 Reykjavik, Iceland

来源：

2013 5TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS) | 2013年

关键词：

Hyperspectral image; sparse regularization; sparse principal component analysis; dimensionality reduction; wavelet; denoising; APPROXIMATION; SHRINKAGE;

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：

Principal Component Analysis (PCA) has widely been used in hyperspectral image analysis as a preprocessing step for further processing. Recently, sparse PCA methods have emerged as a powerful alternative. In this paper we propose a wavelet based sparse PCA method for hyperspectral image denoising. The proposed method is evaluated by using simulated and real data.

引用

页数：4

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