Semantic enhancement and change consistency network for semantic change detection in remote sensing images
Semantic Change Detection (SCD) identifies binary change information and determines the ‘from-to’ types, revealing not only ‘where’ changes occurred but also ‘what’ the changes are. It has become a popular research topic in remote sensing, with many models proposed, yet challenges remain. Semantic e...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2496790 |
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| _version_ | 1849224328527216640 |
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| author | Zhenghao Jiang Biao Wang Peng Zhang Yanlan Wu Zhiyuan Ye Hui Yang |
| author_facet | Zhenghao Jiang Biao Wang Peng Zhang Yanlan Wu Zhiyuan Ye Hui Yang |
| author_sort | Zhenghao Jiang |
| collection | DOAJ |
| description | Semantic Change Detection (SCD) identifies binary change information and determines the ‘from-to’ types, revealing not only ‘where’ changes occurred but also ‘what’ the changes are. It has become a popular research topic in remote sensing, with many models proposed, yet challenges remain. Semantic extraction remains insufficient, and inconsistencies in change features hinder SCD progress. Segment Anything Model 2 (SAM2) has proven effective in extracting global features. However, when processing remote sensing images, SAM2's performance declines. This is particularly evident in images containing diverse ground objects, where interclass similarities are pronounced and intraclass variations are substantial. The model struggles because it lacks semantic information from the local features of adjacent objects. This paper proposes a semantic enhancement and change consistency network(SCNet). By enhancing the model's ability to extract semantic information from complex land-cover classes, we incorporate multi-scale adaptive modules, leveraging the efficient zero-shot capability of SAM2. Specifically, we have designed a semantic alignment module (SA) to enhance change information consistency. This module continuously supervises segmentation and final detection results using interactive self-attention. SCNet achieves state-of-the-art performance on the SECOND, Hi-UCD-mini, and SJH-SCD (our custom) datasets, demonstrating an impressive balance between efficiency and accuracy. The code will be available at https://github.com/XiaoJ058/RS-SCD. |
| format | Article |
| id | doaj-art-e0485ffd1c6c41989a3f670a4f1e729f |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-e0485ffd1c6c41989a3f670a4f1e729f2025-08-25T11:28:31ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2496790Semantic enhancement and change consistency network for semantic change detection in remote sensing imagesZhenghao Jiang0Biao Wang1Peng Zhang2Yanlan Wu3Zhiyuan Ye4Hui Yang5School of Resources and Environmental Engineering, Anhui University, Hefei, People’s Republic of ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei, People’s Republic of ChinaSchool of Artificial Intelligence, Anhui University, Hefei, People’s Republic of ChinaSchool of Artificial Intelligence, Anhui University, Hefei, People’s Republic of ChinaState Grid Information & Telecommunication Group Co., Ltd, Hefei, People’s Republic of ChinaInstitutes of Physical Science and Information Technology, Anhui University, Hefei, People’s Republic of ChinaSemantic Change Detection (SCD) identifies binary change information and determines the ‘from-to’ types, revealing not only ‘where’ changes occurred but also ‘what’ the changes are. It has become a popular research topic in remote sensing, with many models proposed, yet challenges remain. Semantic extraction remains insufficient, and inconsistencies in change features hinder SCD progress. Segment Anything Model 2 (SAM2) has proven effective in extracting global features. However, when processing remote sensing images, SAM2's performance declines. This is particularly evident in images containing diverse ground objects, where interclass similarities are pronounced and intraclass variations are substantial. The model struggles because it lacks semantic information from the local features of adjacent objects. This paper proposes a semantic enhancement and change consistency network(SCNet). By enhancing the model's ability to extract semantic information from complex land-cover classes, we incorporate multi-scale adaptive modules, leveraging the efficient zero-shot capability of SAM2. Specifically, we have designed a semantic alignment module (SA) to enhance change information consistency. This module continuously supervises segmentation and final detection results using interactive self-attention. SCNet achieves state-of-the-art performance on the SECOND, Hi-UCD-mini, and SJH-SCD (our custom) datasets, demonstrating an impressive balance between efficiency and accuracy. The code will be available at https://github.com/XiaoJ058/RS-SCD.https://www.tandfonline.com/doi/10.1080/17538947.2025.2496790Semantic change detectionremote sensingsemantic enhancementchange inconsistenciessemantic align |
| spellingShingle | Zhenghao Jiang Biao Wang Peng Zhang Yanlan Wu Zhiyuan Ye Hui Yang Semantic enhancement and change consistency network for semantic change detection in remote sensing images International Journal of Digital Earth Semantic change detection remote sensing semantic enhancement change inconsistencies semantic align |
| title | Semantic enhancement and change consistency network for semantic change detection in remote sensing images |
| title_full | Semantic enhancement and change consistency network for semantic change detection in remote sensing images |
| title_fullStr | Semantic enhancement and change consistency network for semantic change detection in remote sensing images |
| title_full_unstemmed | Semantic enhancement and change consistency network for semantic change detection in remote sensing images |
| title_short | Semantic enhancement and change consistency network for semantic change detection in remote sensing images |
| title_sort | semantic enhancement and change consistency network for semantic change detection in remote sensing images |
| topic | Semantic change detection remote sensing semantic enhancement change inconsistencies semantic align |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2496790 |
| work_keys_str_mv | AT zhenghaojiang semanticenhancementandchangeconsistencynetworkforsemanticchangedetectioninremotesensingimages AT biaowang semanticenhancementandchangeconsistencynetworkforsemanticchangedetectioninremotesensingimages AT pengzhang semanticenhancementandchangeconsistencynetworkforsemanticchangedetectioninremotesensingimages AT yanlanwu semanticenhancementandchangeconsistencynetworkforsemanticchangedetectioninremotesensingimages AT zhiyuanye semanticenhancementandchangeconsistencynetworkforsemanticchangedetectioninremotesensingimages AT huiyang semanticenhancementandchangeconsistencynetworkforsemanticchangedetectioninremotesensingimages |