We present a filtering framework based on a new statistical model for Single-Look Complex (SLC) Synthetic Aperture Radar (SAR) data, the Scaled Normal-Inverse Gaussian (SNIG). The real and imaginary parts of the SLC image are modeled as mixtures of SNIGs, and the clustering of the mixture components is conducted using a Stochastic Expectation-Maximization (SEM) algorithm. Model parameters are associated to each pixel according to its class, thus producing parametric images of the entire scene. A closed-form Maxmum A Posteriori (MAP) filter then delivers a de-speckled estimate of the image. The method is tested on RADARSAT-2 data, HV polarization, representing images of icebergs surrounded by ocean water off the coast of the Hopen Island (Svalbard archipelago). Post-processing, the iceberg Contrast-to-Noise Ratio (CNR) defined relative to the open water clutter is improved compared to Single-Look Intensity (SLI) image, increasing from a value of 11 to a value of 30 for one of the targets, and the Coefficient of Variation (CV) of the clutter is reduced to a fraction of 0.01 compared to the same reference. We conclude that the proposed method shows potential for improving iceberg detection in open water.
Anca Cristea, Anthony P. Doulgeris, and Torbjørn Eltoft, "A filtering framework for SAR data based on nongaussian statistics and pixel clustering
," Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 1042713 (Presented at SPIE Remote Sensing: September 13, 2017; Published: 4 October 2017); https://doi.org/10.1117/12.2278153.
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