Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images
Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU). The “from-to” information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep lea...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10979855/ |
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| author | Yuhang Zhang Wuxia Zhang Songtao Ding Siyuan Wu Xiaoqiang Lu |
| author_facet | Yuhang Zhang Wuxia Zhang Songtao Ding Siyuan Wu Xiaoqiang Lu |
| author_sort | Yuhang Zhang |
| collection | DOAJ |
| description | Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU). The “from-to” information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multilevel Feature Interaction module (TML-FI), and a Multilevel Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multilevel features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multiscale features, thus improving the model's capability to interpret complex scenes. Finally, ML-FFD integrates the multilevel features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. |
| format | Article |
| id | doaj-art-a81dc3a33aa04e2f82c8144fd4fab0bc |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a81dc3a33aa04e2f82c8144fd4fab0bc2025-08-20T03:08:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118120901210210.1109/JSTARS.2025.356538310979855Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing ImagesYuhang Zhang0Wuxia Zhang1https://orcid.org/0000-0002-0759-2489Songtao Ding2Siyuan Wu3Xiaoqiang Lu4Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, ChinaShaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, ChinaShaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, ChinaCollege of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, ChinaCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, ChinaSemantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU). The “from-to” information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multilevel Feature Interaction module (TML-FI), and a Multilevel Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multilevel features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multiscale features, thus improving the model's capability to interpret complex scenes. Finally, ML-FFD integrates the multilevel features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods.https://ieeexplore.ieee.org/document/10979855/Multiscale analysisremote sensingsemantic change detection (SCD)transformer |
| spellingShingle | Yuhang Zhang Wuxia Zhang Songtao Ding Siyuan Wu Xiaoqiang Lu Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Multiscale analysis remote sensing semantic change detection (SCD) transformer |
| title | Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images |
| title_full | Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images |
| title_fullStr | Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images |
| title_full_unstemmed | Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images |
| title_short | Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images |
| title_sort | spatial temporal semantic feature interaction network for semantic change detection in remote sensing images |
| topic | Multiscale analysis remote sensing semantic change detection (SCD) transformer |
| url | https://ieeexplore.ieee.org/document/10979855/ |
| work_keys_str_mv | AT yuhangzhang spatialtemporalsemanticfeatureinteractionnetworkforsemanticchangedetectioninremotesensingimages AT wuxiazhang spatialtemporalsemanticfeatureinteractionnetworkforsemanticchangedetectioninremotesensingimages AT songtaoding spatialtemporalsemanticfeatureinteractionnetworkforsemanticchangedetectioninremotesensingimages AT siyuanwu spatialtemporalsemanticfeatureinteractionnetworkforsemanticchangedetectioninremotesensingimages AT xiaoqianglu spatialtemporalsemanticfeatureinteractionnetworkforsemanticchangedetectioninremotesensingimages |