Multi-polarimetric SAR image compression based on sparse representation  

作　　者：CHEN Yuan ZHANG Rong YIN Dong 

机构地区：[1]Department of Electronic Engineering and Information Science,University of Science and Technology of China,Hefei 230027,China

出　　处：《Science China(Information Sciences)》2012年第8期1888-1897,共10页中国科学（信息科学）（英文版）

基　　金：supported by National Grand Fundamental Research Program of China(Grant No.2010CB731904);National Natural Science Foundation of China(Grant No.61172154)

摘　　要：The use of sparse representation in signal and image processing has gradually increased over the past few years. Obtaining an over-complete dictionary from a set of signals allows us to represent these signals as a sparse linear combination of dictionary atoms. By considering the relativity among the multi-polarimetric synthetic aperture radar （SAR） images, a new compression scheme for multi-polarimetric SAR image based sparse representation is proposed. The multilevel dictionary is learned iteratively in the 9/7 wavelet domain using a single channel SAR image, and the other channels are compressed by sparse approximation, also in the 9/7 wavelet domain, followed by entropy coding of the sparse coefficients. The experimental results are compared with two state-of-the-art compression methods： SPIHT （set partitioning in hierarchical trees） and JPEG2000. Because of the efficiency of the coding scheme, our method outperforms both SPIHT and JPEG2000 in terms of peak signal-to-noise ratio （PSNR） and edge preservation index （EPI）.The use of sparse representation in signal and image processing has gradually increased over the past few years. Obtaining an over-complete dictionary from a set of signals allows us to represent these signals as a sparse linear combination of dictionary atoms. By considering the relativity among the multi-polarimetric synthetic aperture radar （SAR） images, a new compression scheme for multi-polarimetric SAR image based sparse representation is proposed. The multilevel dictionary is learned iteratively in the 9/7 wavelet domain using a single channel SAR image, and the other channels are compressed by sparse approximation, also in the 9/7 wavelet domain, followed by entropy coding of the sparse coefficients. The experimental results are compared with two state-of-the-art compression methods： SPIHT （set partitioning in hierarchical trees） and JPEG2000. Because of the efficiency of the coding scheme, our method outperforms both SPIHT and JPEG2000 in terms of peak signal-to-noise ratio （PSNR） and edge preservation index （EPI）.

关 键 词：multi-polarimetric SAR image compression sparse representation multilevel dictionary learning edge preservation index （EPI） 

分 类 号：TN958[电子电信—信号与信息处理] TN911.73[电子电信—信息与通信工程]