Erlei Zhang
ICSF: integrating inter-modal and cross-modal learning framework for self-supervised heterogeneous change detection.
Zhang, Erlei; Zong, He; Li, Xinyu; Feng, Mingchen; Ren, Jinchang
Authors
He Zong
Xinyu Li
Mingchen Feng
Professor Jinchang Ren j.ren@rgu.ac.uk
Professor of Computing Science
Abstract
Heterogeneous change detection (HCD) is a process to determine the change information by analyzing heterogeneous images of the same geographic location taken at different times, which plays an important role in remote sensing applications such as disaster response and environmental monitoring. However, the different imaging mechanisms result in different visual appearances in heterogeneous images, making it difficult to accurately detect changes through direct comparison. To address this problem, we propose a novel self-supervised dual-branch framework (ICSF) for HCD that incorporates inter-modal and cross-modal learning. First, in the inter-modal branch, we perform the contrastive learning on heterogeneous images within their respective modalities to learn the robust and discriminative features, rather than relying on the raw spectral or spatial information from these images. Second, in the cross-modal branch, we perform cross-modal reconstruction to ensure the obtained features exhibit consistent comparability, thereby facilitating the extraction of rich information of the real changes within the images. Next, the difference images (DIs) computed from both branches are further refined using a superpixel segmentation strategy to preserve the consistency of differences within the same ground object. Experimental results on five public datasets with different modality combinations and change events demonstrate the effectiveness of the proposed approach in comparison to ten state-of-the-art methods, achieving the best performance with an average overall accuracy of 95.88% and an average Kappa coefficient of 74.20%.
Citation
ZHANG, E., ZONG, H., LI, X., FENG, M. and REN, J. 2025. ICSF: integrating inter-modal and cross-modal learning framework for self-supervised heterogeneous change detection. IEEE transactions on geoscience and remote sensing [online], 63, 501516. Available from: https://doi.org/10.1109/TGRS.2024.3519195
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 10, 2024 |
Online Publication Date | Dec 19, 2024 |
Publication Date | Dec 31, 2025 |
Deposit Date | Jan 7, 2025 |
Publicly Available Date | Jan 7, 2025 |
Journal | IEEE transactions on geoscience and remote sensing |
Print ISSN | 0196-2892 |
Electronic ISSN | 1558-0644 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Volume | 63 |
Article Number | 501516 |
DOI | https://doi.org/10.1109/TGRS.2024.3519195 |
Keywords | Contrastive learning; Dual-branch; Heterogeneous change detection (HCD); Self-supervised learning; Structural relationship |
Public URL | https://rgu-repository.worktribe.com/output/2626172 |
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Copyright Statement
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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