Global structure graph mapping for multimodal change detection

ABSTRACT

Multimodal change detection (MCD) combines multiple remote sensing data sources to realize surface change monitoring, which is essential for disaster evaluation and environmental monitoring. However, due to the ‘incomparable’ features in multimodal data, traditional change detection methods for unimodal (homogenous) data no longer apply. To address this issue, this paper proposes a novel MCD method with global structure graph mapping (GSGM) which extracts the ‘comparable’ structural features between multimodal datasets and constructs a global structure graph (GSG) to express the structure information for each of the multi-temporal images, which are then cross-mapped to the other data domain. The change intensity (CI) is determined by measuring the change of GSGs after mapping and the differences between GSGs and mapped GSGs. The forward and backward CI maps (CIMs) are then fused with the latent low-rank representation method (LLRR), and the change map (CM) is obtained by threshold segmentation. Experiments on five multimodal and four unimodal datasets demonstrate the effectiveness and robustness of the proposed method (source code is made available at https://github.com/rshante0426/GSGM).

KEYWORDS:

• Change detection
• multi-source data
• structural feature
• global structure graph
• image fusion

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

This study was supported by the National Natural Science Foundation of China (Grant 41971313); National Natural Science Foundation of China (Grant 42271411); Scientific Research Innovation Project for Graduate Students in Hunan Province (No. CX20220169); and Research Project on Monitoring and Early Warning Technologies for the Implementation of Land Use Planning in Guangzhou City (2020B0101130009).

Data availability statement

Datasets #1–#2 and #8–#9 that support this study are available at [https://github.com/rshante0426/MCD-datasets]; datasets #3–#5 that support this study are available at [https://www-labs.iro.umontreal.ca/~mignotte/]; and datasets #6–#7 that support this study are available at [https://github.com/MinZHANG-WHU/FDCNN?tab=readme-ov-file].