Bayesian Adaptive Oil Spill Segmentation of SAR Images via Graph Cuts
Sónia Pelizzari(1) and José Bioucas Dias(1)
Instituto de Telecomunicações,
Instituto Superior Técnico,Av. Rovisco Pais,
This paper presents a new Bayesian semisupervised segmentation algorithm aimed at oil spill detection in SAR images, a crucial step in any SAR based automatic oil spill surveillance system .
The data term, i.e., the density of the observed pixel amplitude given the region is modeled as finite mixture of gamma distributions .
This renders robustness to scatter fluctuations inside each region.
The prior is an M-level Markov Random Field defined on a 2D grid enforcing local continuity in a statistical sense .
We adopt the maximum a posteriori (MAP) segmentation criterion.
The optimization problem we are led to is solved efficiently by means of recent graph-cut techniques .
The effectiveness of the proposed method is illustrated with real ERS and ASAR data.
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