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We propose a novel image prior for the non-parametric Bayesian mixture model based unsupervised classification of SAR images. We modified the Normalized Gamma Process prior that constitutes a more general form of the Dirichlet Process prior in order to enclose the contribution of the adjacent pixels into the classification scheme. This yields an image classification prior embedded in a mixture model...
This paper introduces a generalisation of the conventional Maximum Likelihood (ML) texture tracking algorithm in the context of highly heterogeneous PolSAR clutter. The statistical criterion is defined in both uncorrelated and correlated texture cases. Some results on simulated data are computed and an application on temperate glaciers velocity estimation is processed. Finally, some additional improvements...
In the context of image processing and classification, an important problem is the development of accurate models for Synthetic Aperture Radar (SAR) image segmentation. In this paper we propose a highly efficient unsupervised algorithm for image segmentation and changes detection, based on the Generalized Gaussian mixture model. Our work is motivated by the fact that SAR images are highly corrupted...
This paper presents a new method for segmentation of synthetic aperture radar (SAR) images. Based on a non-parametric Bayesian infinite mixture model, Dirichlet process mixture model cluster method is proposed to segment SAR image. The traditional finite mixture model segmentation method is adapted extensively in SAR image segmentation, but the performance and the robustness is not good enough. However,...
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