Automatic PolSAR Segmentation with the U-distribution and Markov Random Fields

Konferenz: EUSAR 2012 - 9th European Conference on Synthetic Aperture Radar
23.04.2012 - 26.04.2012 in Nuremberg, Germany

Tagungsband: EUSAR 2012

Seiten: 4Sprache: EnglischTyp: PDF

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Doulgeris, Anthony P.; Akbari, Vahid; Eltoft, Torbjørn (Department of Physics and Technology, University of Tromsø, 9037 Tromsø, Norway)

A novel unsupervised, non-Gaussian and contextual clustering algorithm is demonstrated for segmentation of Polarimetric SAR images. Previous works have shown the added value of both non-Gaussian modelling and contextual smoothing individually, and goodness-of-fit techniques were introduced to determine the appropriate number of statistically distinct classes. This paper extends our previous work by using the more flexible, two parameter, U-distribution model and includes a Markov Random Field approach for contextual smoothing, without losing the benefits of the goodness-of-fit testing. The proposed, fully automatic, algorithm is demonstrated with both simulated and real data-sets.