An efficient strategy for compressive SAR imaging
In conventional CS-based SAR imaging scheme, the two-dimensional (2-D) scene is vectorized into a long vector, and the physical observation mechanism is represented by a high dimensional matrix. When the scene size increases, the dimensionality of the observation matrix, the computation complexity and the reconstruction time grow tremendously. To solve this problem, a novel separable compressive SAR imaging scheme, named as SCS-SAR, is proposed. In this proposed scheme, the above 2-D observation matrix is replaced by two separable observation matrixes, one in range dimension and the other in cross-range dimension. Then the high-dimensional observation matrix can be reduced to much fewer observation parameters. As a consequence, the computational complexity can be significantly reduced, and the scene of large size can be easily reconstructed with high accuracy. Experimental results demonstrate the superior capability of the proposed imaging scheme.