2010/07/26 by Sónia Pelizzari, Pelizzari, Sónia, José M. Bioucas‐Dias +2
Earth and Planetary Sciences · Engineering · Environmental Science · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #I.4 #Marine and coastal ecosystems #Maritime Navigation and Safety #Oil Spill Detection and Mitigation #msc:I.4 #stat.AP
paper · pdf · doi:10.48550/arxiv.1007.4969
Submitted to IEEE Transactions in Geoscience and Remote Sensing
arxiv created 2010/07/26 · openalex publication_date 2010/07/26 · arxiv updated 2010/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper introduces Bayesian supervised and unsupervised segmentation algorithms aimed at oceanic segmentation of SAR images. The data term, i.e., the density of the observed backscattered signal given the region, is modeled by a finite mixture of Gamma densities with a given predefined number of components. To estimate the parameters of the class conditional densities, a new expectation maximization algorithm was developed. The prior is a multi-level logistic Markov random field enforcing local continuity in a statistical sense. The smoothness parameter controlling the degree of homogeneity imposed on the scene is automatically estimated, by computing the evidence with loopy belief propagation; the classical coding and least squares fit methods are also considered. The maximum a posteriori segmentation is computed efficiently by means of recent graph-cut techniques, namely the α-Expansion algorithm that extends the methodology to an optional number of classes. The effectiveness of the proposed approaches is illustrated with simulated images and real ERS and Envisat scenes containing oil spills.