Principal component analysis for compression of hyperspectral images

Author

Lim, Sunghyun ; Hoon Sohn, Kwang ; Lee, Chulhee

Author_Institution

Dept. of Electr. & Electron. Eng, Yonsei Univ., Seoul, South Korea

Volume

1

fYear

2001

fDate

2001

Firstpage

97

Abstract

In this paper, we explore the possibility to use the principal component analysis for compression of hyperspectral images. When the principal component analysis is applied to AVIRIS data that has 220 channels, we found that most energy is concentrated on a few eigenvalues, indicating that it may be possible to compress hyperspectral images significantly. The performance of the proposed algorithm is evaluated in terms of SNR and classification accuracies of selected classes. Experiments with AVIRIS data show promising results

Keywords

data compression; geophysical signal processing; geophysical techniques; image classification; image coding; multidimensional signal processing; principal component analysis; terrain mapping; AVIRIS; IR; algorithm; data compression; eigenvalues; geophysical measurement technique; hyperspectral image; image classification; image compression; infrared; land surface; principal component analysis; remote sensing; terrain mapping; visible; Compression algorithms; Covariance matrix; Data structures; Eigenvalues and eigenfunctions; Hyperspectral imaging; Image coding; Image storage; Pattern recognition; Principal component analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-7031-7

Type

conf

DOI

10.1109/IGARSS.2001.976068

Filename

976068