Language-Guided Contrastive Learning and Difference Enhancement for Semantic Change Detection in Remote Sensing Images.
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| Title: | Language-Guided Contrastive Learning and Difference Enhancement for Semantic Change Detection in Remote Sensing Images. |
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| Authors: | Hu, Yongli1 (AUTHOR) huyongli@bjut.edu.cn, Ren, Lintian1,2 (AUTHOR), Jiang, Huajie1 (AUTHOR), Guo, Kan1,2 (AUTHOR), Liu, Tengfei1 (AUTHOR), Gao, Junbin2 (AUTHOR), Sun, Yanfeng1 (AUTHOR), Yin, Baocai1 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p964. 20p. |
| Subjects: | Remote-sensing images, Software frameworks |
| Abstract: | Highlights: What are the main findings? We propose LGDENet, a lightweight framework that unifies Language-Guided Contrastive Learning with a difference enhancement mechanism. The method achieves state-of-the-art accuracy on the SECOND and Landsat-SCD datasets while maintaining high computational efficiency compared to foundation model-based approaches. What are the implications of the main findings? The language-guided strategy aligns visual features with text prompts, effectively resolving directional semantic ambiguities in "from–to" transitions. The Difference Enhancement Module (DEM) and the hybrid encoder decouples spatial and channel information to adaptively suppress pseudo-change noise, such as registration errors. Semantic change detection (SCD) in remote sensing images aims not only to localize changed regions but also to identify their specific "from–to" semantic transitions. This task remains challenging due to the inherent semantic ambiguity of spectral changes and the presence of pseudo-change noise. While recent vision–language models have shown promise in remote sensing, existing approaches like RemoteCLIP predominantly focus on static scene classification, lacking the ability to explicitly model dynamic temporal transitions. Other adaptations of foundation models (e.g., AdaptVFMs-RSCD) often rely on heavy backbones, incurring prohibitive computational costs. To address these limitations, this paper proposes LGDENet, a lightweight, end-to-end framework that unifies Language-Guided Temporal Contrastive Learning with a noise-robust difference enhancement mechanism. Specifically, we construct a temporal transition prompt learning strategy that aligns visual difference features with textual descriptions of dynamic processes, thereby resolving directional semantic ambiguities. Furthermore, we introduce a Difference Enhancement Module (DEM) that leverages the channel–spatial decoupling property of depthwise separable convolutions to adaptively isolate and suppress irrelevant variations (e.g., registration errors) before feature fusion. Experiments on the SECOND and Landsat-SCD datasets demonstrate that LGDENet achieves state-of-the-art performance, yielding a semantic F1 score ( F s c d ) of 87.90% and 88.71%, respectively. Moreover, with a modest parameter count of 33.45 M, it offers a superior trade-off between accuracy and efficiency compared to heavy foundation model-based approaches. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We propose LGDENet, a lightweight framework that unifies Language-Guided Contrastive Learning with a difference enhancement mechanism. The method achieves state-of-the-art accuracy on the SECOND and Landsat-SCD datasets while maintaining high computational efficiency compared to foundation model-based approaches. What are the implications of the main findings? The language-guided strategy aligns visual features with text prompts, effectively resolving directional semantic ambiguities in "from–to" transitions. The Difference Enhancement Module (DEM) and the hybrid encoder decouples spatial and channel information to adaptively suppress pseudo-change noise, such as registration errors. Semantic change detection (SCD) in remote sensing images aims not only to localize changed regions but also to identify their specific "from–to" semantic transitions. This task remains challenging due to the inherent semantic ambiguity of spectral changes and the presence of pseudo-change noise. While recent vision–language models have shown promise in remote sensing, existing approaches like RemoteCLIP predominantly focus on static scene classification, lacking the ability to explicitly model dynamic temporal transitions. Other adaptations of foundation models (e.g., AdaptVFMs-RSCD) often rely on heavy backbones, incurring prohibitive computational costs. To address these limitations, this paper proposes LGDENet, a lightweight, end-to-end framework that unifies Language-Guided Temporal Contrastive Learning with a noise-robust difference enhancement mechanism. Specifically, we construct a temporal transition prompt learning strategy that aligns visual difference features with textual descriptions of dynamic processes, thereby resolving directional semantic ambiguities. Furthermore, we introduce a Difference Enhancement Module (DEM) that leverages the channel–spatial decoupling property of depthwise separable convolutions to adaptively isolate and suppress irrelevant variations (e.g., registration errors) before feature fusion. Experiments on the SECOND and Landsat-SCD datasets demonstrate that LGDENet achieves state-of-the-art performance, yielding a semantic F1 score ( F s c d ) of 87.90% and 88.71%, respectively. Moreover, with a modest parameter count of 33.45 M, it offers a superior trade-off between accuracy and efficiency compared to heavy foundation model-based approaches. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060964 |
