Multi-polarimetric SAR image compression based on sparse representation

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).