Unsupervised classification of SAR images using hierarchical agglomeration and EM

Authors:
Koray Kayabol;Vladimir A. Krylov;Josiane Zerubia
Affiliations:
Ariana, INRIA Sophia Antipolis Mediterranee, Sophia Antipolis Cedex, France;Ariana, INRIA Sophia Antipolis Mediterranee, Sophia Antipolis Cedex, France;Ariana, INRIA Sophia Antipolis Mediterranee, Sophia Antipolis Cedex, France
Venue:
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Year:
2011

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Abstract

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.