Despeckling and information extraction from SLC Synthetic Aperture Radar Images using Huber-Markov model and Gauss-Markov Random Fields

Kurzfassung

This paper presents despeckling and information extraction using Single Look Complex (SLC) Synthetic Aperture Radar (SAR) images. The despeckling methods in general use the amplitude or intensity part of the SAR data. In this paper complex SAR images are despeckled using a Tikhonov-like optimization, which enables the modeling of complex data. The likelihood models the distribution of the SAR data, the prior approximates the image. A Huber-Markov random field (HMRF) model and Gauss-Markov random field (GMRF) are used for the scene modeling. The edges and strong scatterers are preserved using the differential part of the data.