Discriminating Ship From Radio Frequency Interference Based on Noncircularity and Non-Gaussianity in Sentinel-1 SAR Imagery

Complex information in single-channel synthetic aperture radar (SAR) imagery is seldom used. This is a common practice based on the conventional resolution theory. However, with the advent of high-resolution SAR sensors, information in the complex data has been found to be of significance for ocean applications. In particular, we note that there is a special type of instrumental artifact in Sentinel-1 images. It is rarely researched and may be attributed to radio frequency interference (RFI). It has similar intensity with ships and can degrade ocean interpretation performance severely. This paper proposes an innovative method to discriminate ships from RFIs based on noncircularity and non-Gaussianity. Among them, noncircularity is calculated based on the measure called normalized noncircularity, and non-Gaussianity is estimated based on the complex generalized Gaussian distribution. The discrimination rationale is analyzed in detail. The experimental procedure is based on Sentinel-1 interferometric wide swath products. Only cross-polarization data are tested since RFIs are quite weak in co-polarization data. It is found that noncircularity and non-Gaussianity can characterize and identify the difference between ships and RFIs. Ships present larger noncircularity and sup-Gaussianity while RFIs are found to exhibit quite low noncircularity and mainly show sub-Caussianity. The proposed method achieves quite good performance. These results show that noncircularity and non-Gaussianity are extremely helpful complements for single-channel SAR imagery interpretation.