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Unsupervised Classification of SAR Images Using Markov Random Fields and {cal G}_{I}^{0} Model

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2 Author(s)
Picco, M. ; Univ. Nac. de Rio Cuarto, Rio Cuarto, Argentina ; Palacio, G.

This letter deals with synthetic aperture radar (SAR) data classification in an unsupervised way. Many models have been proposed to fit SAR data (K, Weibull, Log-normal, etc.), but none of them are flexible enough to model all kinds of surfaces (particularly when there are urban areas present in the image). Our main contribution is the application of a statistical model G0 in a classification process which is shown to be able to model areas with different degrees of heterogeneity. The quality of the classification obtained by mixing this model and a Markovian segmentation is high. We use an iterative conditional estimation method to estimate the parameters of the proposed model.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:8 ,  Issue: 2 )

Date of Publication:

March 2011

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