IMPROVED PRINCIPAL COMPONENT ANALYSIS BASED HYPERSPECTRAL IMAGE COMPRESSION METHOD

被引：1

作者：

Liu, Baisen ^{[1
,2
]}

Zhang, Ye ^{[1
]}

Zhang, Wulin ^{[3
]}

机构：

[1] Harbin Inst Technol, Dept Informat Engn, Harbin, Peoples R China

[2] Heilongjiang Inst Technol, Elect & Informat Engn Dept, Harbin, Peoples R China

[3] Harbin Engn Univ, Dept Informat & Commun Engn Coll, Harbin, Peoples R China

来源：

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

关键词：

Bit allocation; hyperspectral image compression; subspace identification;

D O I：

10.1109/IGARSS.2013.6723065

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：

Due to the huge amount of hyperspectral image (HSI), the compression of HSI is an important topic in remote sensing. The state of art methods use principal component analysis (PCA) as spectral decorrelatior and wavelet transformation as spatial decorrelator. How to determine the number of principal component and how to allocate storage of every principal component are two problems not solved. In this paper, we use hyperspectral signal subspace identification by minimum error (HySime) algorithm to estimate the the number of principal component and eigenvalue as the metric to allocate the storage. The experimental results demonstrate that the proposed algorithms can get better results than traditional PCA based methods.

引用

页码：1478 / 1480

页数：3

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