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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Unsupervised Learning of Generalized Gamma Mixture Model With Application in Statistical Modeling of High-Resolution SAR Images
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Unsupervised Learning of Generalized Gamma Mixture Model With Application in Statistical Modeling of High-Resolution SAR Images

机译：广义伽玛混合模型的无监督学习及其在高分辨率SAR图像统计建模中的应用

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摘要

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 in view of its flexibility and compact form. Specifically, we develop a generalized Gamma mixture model 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 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.
机译：在有效的SAR图像处理，解释和应用中，合成孔径雷达（SAR）图像的精确统计建模是一个关键问题。在本文中，鉴于其Gamma分布的灵活性和紧凑形式，在有限混合模型的框架内设计了一种半参数方法。具体来说，我们开发了一种广义的Gamma混合模型，以对高分辨率SAR图像进行有效的统计分析，并证明了此类混合物的可识别性。通过将基于直方图的期望条件最大化算法与Figueiredo-Jain算法结合起来，得出了一种低复杂度的无监督估计方法。这导致了数值最大似然（ML）估计器，该估计器可以同时确定组分参数的ML估计值和混合物组分的最佳数量。最后，通过对各种高分辨率SAR图像进行的实验验证了该方法的最新性能。

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