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24 August 2010 Hyperspectral image compression algorithm using wavelet transform and independent component analysis
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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.
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Mingyi He, Lin Bai, and Fatima Syeda Narjis "Hyperspectral image compression algorithm using wavelet transform and independent component analysis", Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781005 (24 August 2010); https://doi.org/10.1117/12.863149
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