ChangeDA: depth-augmented multi-task network for remote sensing change detection via differential analysis.

Meng, Jiangtao; Xu, Xinying; Zhang, Zhe; Li, Pengyue; Xie, Gang; Ren, Jinchang; Zheng, Yuxuan

Abstract

In the field of Remote Sensing Change Detection (RSCD), accurately identifying significant changes between bitemporal images is essential for environmental monitoring, urban planning, and disaster assessment. In recent years, advancements in deep learning for computer vision have transformed RSCD, significantly enhancing its effectiveness. However, existing methods often overlook the importance of depth information, focusing primarily on two-dimensional information. This limits their ability to capture subtle changes and structural details in three-dimensional space. To address these limitations, we introduce ChangeDA—a depth-augmented multi-task network designed to enhance the effectiveness of RSCD. ChangeDA introduces a depth encoder module to extract implicit depth information from optical images, enabling the utilization of 3D structural information without reliance on external data sources. Through the Depth Infusion Module (DIM), depth information is integrated into the dual-temporal feature maps, significantly enhancing the network’s ability to perceive changes in three-dimensional spatial structures. Additionally, ChangeDA includes a Differential Feature Extractor (DFE) tailored to pinpoint differential features between sequential images, and an Adaptive All feature Fusion (AAFF) strategy that significantly improves recognition accuracy and generalization capability through cross-level feature integration. Performance evaluations on four prominent single-modal datasets—LEVIR-CD, S2-Looking, WHU-CD, and SYSU-CD—yielded state-of-the-art F1-scores of 92.27%, 66.42%, 94.12%, and 82.74%, respectively. Furthermore, ChangeDA also achieved outstanding results on the multi-modal 3DCD dataset, with an F1 score of 63.52% in 2D CD and an RMSE of 1.20 in the 3D CD task. These results demonstrate ChangeDA’s robust adaptability across diverse targets and real-world scenarios.

Citation

MENG, J., XU, X., ZHANG, Z., LI, P., XIE, G., REN, J. and ZHENG, Y. [2025]. ChangeDA: depth-augmented multi-task network for remote sensing change detection via differential analysis. IEEE Transactions on geoscience and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/TGRS.2025.3532468

• Journal Article Type: Article
• Acceptance Date: Jan 20, 2025
• Online Publication Date: Jan 20, 2025
• Deposit Date: Jan 28, 2025
• Publicly Available Date: Jan 28, 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
• DOI: https://doi.org/10.1109/tgrs.2025.3532468
• Keywords: Change detection; Multi-task; Multimodal; Depth estimation; Optical flow; Remote sensing
• Public URL: https://rgu-repository.worktribe.com/output/2669428