Sidelobe Suppression for High-Resolution SAR Imagery Based on Spectral Reshaping and Feature Statistical Difference

Sidelobe suppression is a crucial preprocessing technique for Synthetic Aperture Radar (SAR) image analysis. The presence of strong scattering targets generates sidelobes that can interfere the targets with relatively weak scattering. The overlapping of multiple strong cross-shaped sidelobes may even generate fake targets, significantly influencing the accuracy of SAR target detection and recognition. Among existing sidelobe suppression methods, the Spectral Reshaping Sidelobe Reduction (SRSR) method has shown promising results. It separates the mainlobe and sidelobes through altering the sidelobe direction while preserving the SAR image resolution. However, this method exhibits limitations in effectively suppressing strong cross-shaped sidelobes. It also introduces additional sidelobes, blurring the surroundings of the scattering points. This paper proposes an improved SRSR method to resolve this disadvantage. It constructs a feature image representing the superposition of sidelobes. This is achieved by analyzing the statistical differences of complex data between orthogonal and non-orthogonal sidelobe regions before and after spectral reshaping. Further modulus selection ensures that the feature image only contains the sidelobe information that needs to be eliminated. The proposed method successfully resolves the drawback of introducing new sidelobes in the original SRSR while achieving better suppression of strong cross-shaped sidelobes. Experimental results on airborne and spaceborne SAR images demonstrate that the proposed method outperforms other state-of-the-art techniques. Improved peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) in both range and azimuth directions and smaller image entropy can be achieved by our method. Due to its superior sidelobe suppression capability, the SAR images processed by our method exhibit significantly improved accuracy in target detection.