Hyperspectral Image Compression Algorithm Using Wavelet Transform and Independent Component Analysis

被引：0

作者：

He, Mingyi ^{[1
]}

Bai, Lin ^{[1
]}

Narjis, Fatima Syeda ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Elect & Informat, Shaanxi Key Lab Informat Acquisit & Proc, Xian 710129, Peoples R China

来源：

SATELLITE DATA COMPRESSION, COMMUNICATIONS, AND PROCESSING VI | 2010年 / 7810卷

关键词：

segmented independent component analysis; principal components analysis; discrete wavelet transform; hyperspectral image compression;

D O I：

10.1117/12.863149

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：

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.

引用

页数：7

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