Automatic Segmentation of SAR imagery Using Mixture Models

Synthetic Aperture Radar (SAR) image segmentation underpins target detection, land cover classification, and environmental monitoring, yet remains challenging due to speckle, non-Gaussian backscatter statistics, and outliers. This paper presents a comparative evaluation of mixture-model–based segmentation tailored to SAR, with a focus on Radarsat Constellation Mission (RCM) imagery. We propose a segmentation algorithm that selects one of three statistical mixture models—Rayleigh, Gamma, or Lognormal—to model SAR backscatter and produce soft (posterior) segmentations, followed by posterior thresholding and optional MRF‑ICM post‑processing to enhance spatial coherence and suppress speckle-induced errors. We compare against standard operational approaches, threshold-based methods (CFAR, multi-threshold Otsu) and conventional mixture-model labeling that designates the largest-scale component as the target. On RCM data, the Rayleigh Mixture Model (RMM) is the strongest: at target pixels, the posterior probability of the largest-mean component is typically very close to 1, allowing a single Rayleigh component to capture the main body of the iceberg reliably. Unlike threshold-based baselines that yield hard segmentations, our Mixture Model (MM) approach outputs soft posteriors, enabling principled HH/HV fusion and downstream machine learning (ML). These results underscore the promise of RMM for robust iceberg detection; future work will integrate Rayleigh-based posterior features with lightweight ML classifiers to further improve performance across sensors and conditions.