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An hierarchical approach for model-based classification of SAR images

Abstract : We propose an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images based on Classification Expectation-Maximization (CEM). We combine the CEM algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL) to get rid of the initialization and the model order selection problems of the EM algorithm. We exploit a mixture of Nakagami densities for amplitudes and a Multinomial Logistic (MnL) latent model for class labels to obtain spatially smooth class segments. We test our algorithm on TerraSAR-X data.
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Contributor : Koray Kayabol Connect in order to contact the contributor
Submitted on : Tuesday, April 10, 2012 - 10:17:19 PM
Last modification on : Wednesday, February 2, 2022 - 3:51:04 PM
Long-term archiving on: : Wednesday, July 11, 2012 - 2:56:15 AM


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  • HAL Id : hal-00686658, version 1



Koray Kayabol, Josiane Zerubia. An hierarchical approach for model-based classification of SAR images. 20th Signal Processing and Communications Applications Conference, Apr 2012, Mugla, Turkey. ⟨hal-00686658⟩



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