Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning
Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning
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DOI：
10.1109/tgrs.2022.3229027
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发表时间：
2022-10
影响因子：
8.2
通讯作者：
Hongruixuan Chen;N. Yokoya;Chen Wu;Bo Du
中科院分区：
文献类型：
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作者：
Hongruixuan Chen;N. Yokoya;Chen Wu;Bo Du
无监督多峰变化检测是一个具有实际意义和挑战性的课题，可以在时间敏感的紧急情况下发挥重要作用。为了解决多模式遥感图像由于模式异质性而无法直接进行比较的问题，我们利用了多模式图像中两种类型的与模式无关的结构关系。特别地，我们提出了一个结构关系图表示学习框架来度量两个结构关系的相似性。首先，通过基于对象的图像分析方法，对经过预处理的多峰图像对生成结构图。然后，提出了一种结构关系图卷积自动编码器(SR-GCAE)，用于从图中学习稳健且具有代表性的特征。为了使所学习的表示法适用于结构关系相似性度量，提出了两个针对重构顶点信息和边信息的损失函数。随后，根据学习的图表示计算两个结构关系的相似度，并基于相似度生成两个差异图像。在获得差值图像后，提出了一种自适应融合策略来融合两幅差值图像。最后，采用基于形态滤波的后处理方法对检测结果进行细化。在6个不同模式组合的数据集上的实验结果证明了该方法的有效性。
Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To address the challenge that multimodal remote sensing images cannot be directly compared due to their modal heterogeneity, we take advantage of two types of modality-independent structural relationships in multimodal images. In particular, we present a structural relationship graph representation learning framework for measuring the similarity of the two structural relationships. First, structural graphs are generated from preprocessed multimodal image pairs by means of an object-based image analysis approach. Then, a structural relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn robust and representative features from graphs. Two loss functions aiming at reconstructing vertex information and edge information are presented to make the learned representations applicable for structural relationship similarity measurement. Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations, and two difference images are generated based on the similarity levels. After obtaining the difference images, an adaptive fusion strategy is presented to fuse the two difference images. Finally, a morphological filtering-based postprocessing approach is employed to refine the detection results. Experimental results on six datasets with different modal combinations demonstrate the effectiveness of the proposed method.