The performance of synthetic aperture radar (SAR) image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method. Considering the above mentioned issues, this paper proposes an unsupervised framework for SAR image change detection in which each pixel is taken as a vertex and the collection of pixels is represented by hyperedges in a hypergraph. Thus, the task of SAR image change detection is formulated as the problem of hypergraph matching and hypergraph partition. First, instead of using the K nearest neighbour rule, we propose a coupling neighbourhood based on the spatial-intensity constraint to gather the neighbours for each vertex. Then, hyperedges are constructed on the pixels and their coupling neighbours. The weight of hyperedge is computed via the sum of the patch-based pairwise affinities within the hyperedge. Through modelling the two hypergraphs on the bi-temporal SAR images, not only the change level of vertices is described, but also the changes of local grouping and consistency within hyperedge are excavated. Thus, the difference image with a good separability can be obtained by matching each vertex and hyperedge between the two hypergraphs. Finally, a generalized hypergraph partition technique is employed to classify changed and unchanged areas in the generated difference image. Experimental results on real SAR datasets confirm the validity of the proposed framework in improving the robustness and accuracy of change detection.

合成孔径雷达（SAR）图像变化检测的性能主要取决于差分图像的质量和分类方法的准确性。考虑到上述问题，本文提出了一种无监督的SAR图像变化检测框架，其中每个像素都作为一个顶点，像素集合由超图中的超边缘表示。因此，将SAR图像变化检测的任务表述为超图匹配和超图分割问题。首先，不要使用K最近邻规则，我们建议基于空间强度约束的耦合邻域，以收集每个顶点的邻域。然后，在像素及其耦合邻居上构建超边缘。hyperedge的权重是通过hyperedge中基于补丁的成对亲和力之和来计算的。通过在双时相SAR图像上对两个超图进行建模，不仅描述了顶点的变化水平，而且挖掘了超边缘内局部分组和一致性的变化。因此，通过匹配两个超图之间的每个顶点和超边缘，可以获得具有良好可分离性的差异图像。最后，采用广义超图分割技术对生成的差异图像中变化和未变化的区域进行分类。