A Robust Man-Made Target Detection Method Based on Relative Spectral Stationarity for High-Resolution SAR Images

The generality and robustness of a man-made target detection method are of high practical value in synthetic aperture radar (SAR) applications. However, current methods hardly adapt to different SAR images simultaneously because data property and target characteristics are not identical in images with various resolutions and scenes. The key to achieving high generality and robustness is extracting stable and invariant information in different SAR images. This article analyzes the scattering of man-made targets and natural backgrounds in SAR images and assumes that the noise characteristics are relatively stable and invariant when resolution and observing background change. Then, the relative spectral stationarity (RSS) is proposed based on 2-D spectrum analysis to measure the distance between the observed data and a manually generated noise. RSS takes the manually generated noise with known and definite properties as a standard, so it is irrelevant to image parameters and scenes. A low RSS indicates that the characteristics of observing data are dominated by noise, whereas a high value means that there may be targets in the observing data weakening the noise characteristics. An efficient segmentation algorithm Sparsity-based Format-free Segmentation within error e (SFFe) is proposed to process the RSS map and complete the detection method. SAR images with resolutions ranging from 0.1 to 8 m are employed as testing data. Various testing scenes are constructed to simulate different practical conditions. Experimental results validate that the proposed RSS -based method works well in SAR images with different resolutions, bands, and observing scenes, obtaining reliable and robust detection results and outperforming canonical methods on various criteria.