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首页> 外文期刊>Image Processing, IEEE Transactions on >Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering
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Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering

机译：基于图像融合和模糊聚类的合成孔径雷达图像变化检测

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摘要

This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.
机译：本文提出了一种基于图像融合策略和新颖模糊聚类算法的合成孔径雷达图像无监督无分布变化检测方法。引入图像融合技术以通过使用来自均值比图像和对数比图像的互补信息来生成差分图像。为了限制背景信息并增强融合差分图像中变化区域的信息，选择了基于平均算子和最小局部能量的小波融合规则，以融合低频段和高频段的小波系数。频段。提出了一种改进的模糊局部信息C-均值聚类算法，对融合差分图像中的变化和未变化区域进行分类。它以新颖的模糊方式合并了有关空间上下文的信息，目的是增强更改的​​信息并减少斑点噪声的影响。在真实SAR图像上的实验表明，该图像融合策略融合了对数比算子和均值比算子的优点，取得了较好的性能。通过改进的模糊聚类算法获得的变化检测结果显示出比其先前存在的误差低的误差。

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