Unsupervised Learning of Generalized Gamma Mixture Model with Application in Statistical Modeling of High-Resolution SAR Images

Abstract : The accurate statistical modeling of synthetic aperture radar (SAR) images is a crucial problem in the context of effective SAR image processing, interpretation and application. In this paper a semi-parametric approach is designed within the framework of finite mixture models based on the generalized Gamma distribution (GΓD) in view of its flexibility and compact form. Specifically, we develop a generalized Gamma mixture model (GΓMM) to implement an effective statistical analysis of high-resolution SAR images and prove the identifiability of such mixtures. A low-complexity unsupervised estimation method is derived by combining the proposed histogram-based expectation-conditional maximization (ECM) algorithm and the Figueiredo-Jain algorithm. This results in a numerical maximum likelihood (ML) estimator that can simultaneously determine the ML estimates of component parameters and the optimal number of mixture components. Finally, the state-of-the-art performance of this proposed method is verified by experiments with a wide range of high-resolution SAR images. Index Terms Synthetic aperture radar (SAR) images, finite mixture model, generalized Gamma distribution, expectation-conditional maximization (ECM) algorithm, minimum message length (MML), probability density function estimation , unsupervised learning.
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IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (4), pp.2153-2170
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Heng-Chao Li, Vladimir A. Krylov, Ping-Zhi Fan, Josiane Zerubia, William J. Emery. Unsupervised Learning of Generalized Gamma Mixture Model with Application in Statistical Modeling of High-Resolution SAR Images. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (4), pp.2153-2170. 〈hal-01217654〉

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