Fourier domain structural relationship analysis for unsupervised multimodal change detection
Fourier domain structural relationship analysis for unsupervised multimodal change detection
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DOI：
10.1016/j.isprsjprs.2023.03.004
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发表时间：
2023-03-11
影响因子：
12.7
通讯作者：
Chini, Marco
中科院分区：
文献类型：
--
作者：
Chen, Hongruixuan;Yokoya, Naoto;Chini, Marco
多模态遥感图像的变化检测已成为遥感界一个越来越有趣和具有挑战性的主题，它可以在灾害响应等时间敏感的应用中发挥重要作用。然而，模态异质性问题使得直接比较多模态图像变得困难。本文提出了一种用于无监督多模态变化检测（FD-MCD）的傅立叶域结构关系分析框架，该框架利用了模态无关的局部和非局部结构关系。与分析多模态图像原始域中的结构关系的大多数现有方法不同，所提出的框架中的三个关键部分是在（图）傅立叶域上实现的。首先，提出了在傅立叶域中计算的局部频率一致性度量来确定局部结构差异。然后，为变化前和变化后的图像构建非局部结构关系图。然后将两个图变换到图傅立叶域，并通过图谱卷积对每个顶点建模高阶顶点信息，其中切比雪夫多项式用作传递函数来传递 K 跳局部邻域顶点信息。通过比较过滤后的图表示来获得非局部结构差异图。最后，设计了一种基于频率解耦的自适应融合方法，以有效融合局部和非局部结构差异图。在具有不同模态组合和变化事件的五个真实数据集上进行的实验表明了所提出的框架的有效性。
Change detection on multimodal remote sensing images has become an increasingly interesting and challenging topic in the remote sensing community, which can play an essential role in time-sensitive applications, such as disaster response. However, the modal heterogeneity problem makes it difficult to compare the multimodal images directly. This paper proposes a Fourier domain structural relationship analysis framework for unsupervised multimodal change detection (FD-MCD), which exploits both modality-independent local and nonlocal structural relationships. Unlike most existing methods analyzing the structural relationship in the original domain of multimodal images, the three critical parts in the proposed framework are implemented on the (graph) Fourier domain. Firstly, a local frequency consistency metric calculated in the Fourier domain is proposed to determine the local structural difference. Then, the nonlocal structural relationship graphs are constructed for pre-change and post-change images. The two graphs are then transformed to the graph Fourier domain, and high-order vertex information is modeled for each vertex by graph spectral convolution, where the Chebyshev polynomial is applied as the transfer function to pass K-hop local neighborhood vertex information. The nonlocal structural difference map is obtained by comparing the filtered graph representations. Finally, an adaptive fusion method based on frequency-decoupling is designed to effectively fuse the local and nonlocal structural difference maps. Experiments conducted on five real datasets with different modality combinations and change events show the effectiveness of the proposed framework.