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Unsupervised Classification of SAR Images Using Hierarchical Agglomeration and EM

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 7252)

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.

Keywords

  • High resolution SAR
  • TerraSAR-X
  • classification
  • texture
  • multinomial logistic
  • Classification EM
  • hierarchical agglomeration

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Kayabol, K., Krylov, V.A., Zerubia, J. (2012). Unsupervised Classification of SAR Images Using Hierarchical Agglomeration and EM. In: Salerno, E., Çetin, A.E., Salvetti, O. (eds) Computational Intelligence for Multimedia Understanding. MUSCLE 2011. Lecture Notes in Computer Science, vol 7252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32436-9_5

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  • DOI: https://doi.org/10.1007/978-3-642-32436-9_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32435-2

  • Online ISBN: 978-3-642-32436-9

  • eBook Packages: Computer ScienceComputer Science (R0)