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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model
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A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model

机译：基于图割和广义高斯模型的SAR图像变化检测

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摘要

In this letter, a robust and fast unsupervised change-detection framework is proposed for synthetic aperture radar (SAR) images. It contains three aspects. First, a robust difference image is constructed with the idea of probability patch-based, and it can suppress the speckle effects on the changed regions and enhance the change information synchronously. Then, each class of the difference image is modeled by generalized Gaussian distribution (GGD), and its parameters are learned by the expectation-maximization algorithm. Moreover, the graph-cut algorithm is employed on the difference image to extract the spatial prior information, based on which the parameters of GGD are initialized well via the fuzzy $c$-means algorithm. Finally, the Bayesian inference for maximum a posteriori performs the final detection. Experimental results on simulated and real SAR data sets confirm the robustness and accuracy of the proposed algorithm in which graph-cut and GGD make great contribution on improving the accuracy of detection and speed of algorithm.
机译：在这封信中，为合成孔径雷达（SAR）图像提出了一种鲁棒且快速的无监督变化检测框架。它包含三个方面。首先，基于概率补丁的思想构造了鲁棒的差异图像，可以抑制变化区域的斑点效应，并同步增强变化信息。然后，利用广义高斯分布（GGD）对差异图像的每一类进行建模，并通过期望最大化算法学习其参数。此外，在差异图像上采用图割算法提取空间先验信息，然后通过模糊$ c $ -means算法很好地初始化GGD的参数。最后，最大后验的贝叶斯推断执行最终检测。模拟和真实SAR数据集的实验结果证实了所提算法的鲁棒性和准确性，其中图割和GGD对提高检测精度和算法速度做出了巨大贡献。

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