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Proceedings Paper

Hyperspectral image compression algorithm using wavelet transform and independent component analysis
Author(s): Mingyi He; Lin Bai; Fatima Syeda Narjis
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Paper Abstract

A lossy hyperspectral images compression algorithm based on discrete wavelet transform (DWT) and segmented independent component analysis is presented in this paper. Firstly, bands are divided into different groups based on the correlation coefficient. Secondly, maximum noise fraction (MNF) method and maximum likelihood estimation are used to estimate dimensionality of data in each group. Based on the result of dimension estimation, ICA and DWT are deployed in spectral and spatial directions respectively. Finally, SPIHT and arithmetic coding are applied to the transformation coefficients respectively, achieving quantization and entropy coding. Experimental results on 220 band AVIRIS hyperspectral data show that the proposed method achieves higher compression ratio and better analysis capability as compared with PCA and SPIHT algorithms.

Paper Details

Date Published: 24 August 2010
PDF: 7 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781005 (24 August 2010); doi: 10.1117/12.863149
Show Author Affiliations
Mingyi He, Northwestern Polytechnical Univ. (China)
Lin Bai, Northwestern Polytechnical Univ. (China)
Fatima Syeda Narjis, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)

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