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Communication Dans Un Congrès Année : 2012

Unsupervised Classification of SAR Images Using Hierarchical Agglomeration and EM

Koray Kayabol
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Vladimir A. Krylov
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Josiane Zerubia

Résumé

We implement an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images. The foundation of algorithm is based on Classification Expectation-Maximization (CEM). To get rid of two drawbacks of EM type algorithms, namely the initialization and the model order selection, we combine the CEM algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL). We exploit amplitude statistics in a Finite Mixture Model (FMM), and a Multinomial Logistic (MnL) latent class label model for a mixture density to obtain spatially smooth class segments. We test our algorithm on TerraSAR-X data.
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Dates et versions

hal-00782641 , version 1 (31-01-2013)

Identifiants

Citer

Koray Kayabol, Vladimir A. Krylov, Josiane Zerubia. Unsupervised Classification of SAR Images Using Hierarchical Agglomeration and EM. Computational Intelligence for Multimedia Understanding MUSCLE 2011, Dec 2011, Pisa, Italy. pp.54-65, ⟨10.1007/978-3-642-32436-9_5⟩. ⟨hal-00782641⟩
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