Multi-polarimetric SAR image compression based on sparse representation |
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Authors: | CHEN Yuan ZHANG Rong&YIN Dong |
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Affiliation: | Department of Electronic Engineering and Information Science,University of Science and Technology of China,Hefei 230027,China |
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Abstract: | 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). |
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Keywords: | multi-polarimetric SAR image compression sparse representation multilevel dictionary learning edge preservation index(EPI) |
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