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

Heng-Chao Li (1) , Vladimir A. Krylov (2) , Ping-Zhi Fan (1) , Josiane Zerubia (3) , William J. Emery (4)

1 SWJTU - Southwest Jiaotong University
2 DITEN - Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture
3 AYIN - Models of spatio-temporal structure for high-resolution image processing
4 Department of Aerospace Engineering Sciences

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.

Mots clés: unsupervised learning.; expectation-conditional maximization (ECM) algorithm; generalized Gamma distribution; finite mixture model; Synthetic aperture radar (SAR) images; minimum message length (MML); probability density function estimation

Domaines: Traitement du signal et de l'image [eess.SP]

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, 2016, 54 (4), pp.2153-2170. ⟨hal-01217654⟩