Modelling sea clutter in Sar images using Laplace-Rician distribution

This paper presents a novel statistical model for the characterisation of synthetic aperture radar (SAR) images of the sea surface. The analysis of ocean surface is widely performed using satellite imagery as it produces information for wide areas under various weather conditions. An accurate SAR amplitude distribution model enables better results in despeckling, ship detection/tracking and so forth. In this paper, we develop a new statistical model, namely the LaplaceRician distribution for modelling amplitude SAR images of the sea surface. The proposed statistical model is based on Rician distribution to model the amplitude of a complex SAR signal, the in-phase and quadrature components of which are assumed to be Laplace distributed. The Laplace-Rician model is investigated for SAR images of the sea surface from COSMO-SkyMed and Sentinel-1 in comparison to state-of-the-art statistical models such as mathcal{K}, lognormal and Weibull distributions. In order to decide on the most suitable model, statistical significance analysis via Kullback-Leibler divergence and Kolmogorov-Smirnov statistics is performed. The results show a superior modelling performance of the proposed model for all of the utilised images.