Fourier domain structural relationship analysis for unsupervised multimodal change detection

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 198; pp. 99 - 114
• Main Authors: Chen, Hongruixuan, Yokoya, Naoto, Chini, Marco
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2023
• Subjects: Change detection; data collection; domain; Fourier domain; Graph spectral convolution; Multimodal remote sensing images; photogrammetry; Structural relationship; Multimodal remote sensing images; Graph spectral convolution; Fourier domain; Structural relationship; Change detection
• ISSN: 0924-2716
• DOI: 10.1016/j.isprsjprs.2023.03.004

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.