期刊
SCIENCE CHINA-INFORMATION SCIENCES
卷 55, 期 8, 页码 1888-1897出版社
SCIENCE PRESS
DOI: 10.1007/s11432-012-4612-9
关键词
multi-polarimetric SAR image compression; sparse representation; multilevel dictionary learning; edge preservation index (EPI)
资金
- National Grand Fundamental Research Program of China [2010CB731904]
- National Natural Science Foundation of China [61172154]
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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