ChangeDA: Depth-augmented multitask network for remote sensing change detection via differential analysis
Meng, J.; Xu, X.; Zhang, Z.; Li, P.; Xie, G.; Ren, J.; Zheng, Y. (2025). ChangeDA: Depth-augmented multitask network for remote sensing change detection via differential analysis. IEEE Trans. Giosci. Remote Sens. 63: 1-19. https://dx.doi.org/10.1109/tgrs.2025.3532468
In: IEEE transactions on geoscience and remote sensing. Institute of Electrical and Electronics Engineers: New York, N.Y.. ISSN 0196-2892; e-ISSN 1558-0644, more
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| Authors | | Top |
- Meng, J.
- Xu, X.
- Zhang, Z.
- Li, P.
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- Xie, G.
- Ren, J.
- Zheng, Y.
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| Abstract |
In the field of remote sensing change detection (RSCD), accurately identifying significant changes between bi-temporal images is essential for environmental monitoring, urban planning, and disaster assessment. In recent years, advancements in deep learning for computer vision (CV) have transformed RSCD, significantly enhancing its effectiveness. However, existing methods often overlook the importance of depth information, focusing primarily on 2-D information. This limits their ability to capture subtle changes and structural details in 3-D space. To address these limitations, we introduce ChangeDA—a depth-augmented multitask 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 3-D 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 3-D spatial structures. In addition, 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, S2Looking, WHU-CD, and SYSU-CD—yielded state-of-the-art (SOTA) F1 -scores of 92.27%, 66.42%, 94.12%, and 82.74%, respectively. Furthermore, ChangeDA also achieved outstanding results on the multimodal 3DCD dataset, with an F1 score of 63.52% in 2-D CD and an RMSE of 1.20 in the 3-D CD task. These results demonstrate ChangeDA’s robust adaptability across diverse targets and real-world scenarios. |
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