Compressive sensing-based sparse representation analysis for remote sensing SAR imagery

The signal processing technology of Compressive Sensing (CS) overturns the limitation of the traditional sensing method that only high sampling rate can achieve high resolution. The compressive sensing method saves internal storage and resources of information processing due to the small amount of data processed. Applying CS to spaceborne Synthetic Aperture Radar (SAR) system can effectively reduce the pressure of hardware platform. Sparse representation of SAR image is a prerequisite for CS application in remote sensing SAR imagery, which is analyzed quantitatively under different orthogonal bases and representation approach. The effect analysis is measured by the evaluation indexes such as peak signal-to-noise ratio, structural similarity and edge retention index. The simulation results show that the effect of sparse representation can meet the quality and structure requirements of SAR images in most cases. The conclusion verifies that SAR images satisfy CS requirement for sparsity, which supports the further study of CS reconstruction application in spaceborne SAR image.