Title :
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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-7031-7
DOI :
10.1109/IGARSS.2001.976068
