SAR Image Segmentation via Hierarchical Region Merging and Edge Evolving With Generalized Gamma Distribution

This letter proposes a novel segmentation algorithm for synthetic aperture radar (SAR) images based on hierarchical region merging and edge evolving. To cope with the influence of speckle in SAR images, a statistical stepwise criterion, the loss of log-likelihood function (LLF) of image partition, is utilized for region merging. For this merging procedure, precise distributions of image partitions are essential, and we employ the generalized gamma distribution (GΓD) for modeling SAR images. Besides, the traditional region merging methods often suffer from the initial image partition that may lead to coarse segment shapes. It motivates us introducing a novel edge evolving scheme into the segmentation algorithm. It consists of two iterative steps: the evolution of edge pixels with a maximum likelihood (ML) criterion and that with a maximum a posterior (MAP) criterion using a Markov random field (MRF) model. The performance of the proposed algorithm is validated on two actual SAR images from the AIRSAR and EMISAR systems.

[1]  Heng-Chao Li,et al.  On the Empirical-Statistical Modeling of SAR Images With Generalized Gamma Distribution , 2011, IEEE Journal of Selected Topics in Signal Processing.

[2]  Stan Z. Li.  Markov Random Field Modeling in Image Analysis , 2009, Advances in Pattern Recognition.

[3]  Morris Goldberg,et al.  Hierarchy in Picture Segmentation: A Stepwise Optimization Approach , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Gabriel Vasile,et al.  Hierarchical segmentation of Polarimetric SAR images using heterogeneous clutter models , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[5]  S. Quegan,et al.  Understanding Synthetic Aperture Radar Images , 1998 .

[6]  Jean-Marie Nicolas.  1 - Introduction aux Statistiques de deuxième espèce : applications des Logs-moments et des Logs-cumulants à l'analyse des lois d'images radar , 2002 .

[7]  Gabriele Moser,et al.  A K-Wishart Markov random field model for clustering of polarimetric SAR imagery , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[8]  Ridha Touzi,et al.  Segmentation of textured polarimetric SAR scenes by likelihood approximation , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[10]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Gabriele Moser,et al.  Modeling the Statistics of High Resolution Sar Images Modeling the Statistics of High Resolution Sar Images Modélisation Des Statistiques Des Images Radar (rso) Haute Résolution , 2022 .

[12]  Philip Chan,et al.  Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[13]  E. Stacy.  A Generalization of the Gamma Distribution , 1962 .

[14]  Frédéric Galland,et al.  Minimum description length synthetic aperture radar image segmentation , 2003, IEEE Trans. Image Process..

[15]  Amar Mitiche,et al.  Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model , 2006, IEEE Transactions on Image Processing.