Fast compression algorithm of SAR image based on compressed sensing

A novel SAR image compress and reconstruction algorithm based on compressive sampling (CS) is proposed in this paper. Firstly, the image is represented sparsely by G-level contourlet. Secondly, a Gaussian random matrices that proximate QR factorization is constructed to measure the high frequency coefficients and to realize data compression. Lastly, a modified Sparsity Adaptive Matching Pursuit algorithm(SAMP) is used to realize the precise reconstruction of SAR image. Experimental results demonstrate that the proposed algorithm can get better reconstruction performances and the convergence of the algorithm is much faster than the existed algorithms.