Bi-temporal Gaussian feature dependency guided change detection in remote sensing images
Change detection (CD) enables the identification of alterations between images of the same area captured at different times. However, existing CD methods still struggle to mitigate pseudo-changes resulting from domain information differences in multi-temporal images and instances of detail errors ca...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2508224 |
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| _version_ | 1839608336881811456 |
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| author | Yi Xiao Bin Luo Jun Liu Xin Su Wei Wang |
| author_facet | Yi Xiao Bin Luo Jun Liu Xin Su Wei Wang |
| author_sort | Yi Xiao |
| collection | DOAJ |
| description | Change detection (CD) enables the identification of alterations between images of the same area captured at different times. However, existing CD methods still struggle to mitigate pseudo-changes resulting from domain information differences in multi-temporal images and instances of detail errors caused by the loss and contamination of detail features during the upsampling process in the network. To better alleviate these problems, we propose a bi-temporal Gaussian distribution feature-dependent (BGFD) network. Specifically, we first introduce the Gaussian noise domain disturbance (GNDD) module, which approximates the distribution using image statistical features to characterize domain information and samples noise to perturb the network for learning redundant domain information, mitigating domain information differences. Additionally, within the feature dependency facilitation (FDF) module, we integrate a novel mutual information difference loss ([Formula: see text]) and more sophisticated attention mechanisms to enhance the capabilities of the network, ensuring the acquisition of essential domain information. Subsequently, we have designed a novel detail feature compensation (DFC) module, which compensates for detail feature loss and contamination introduced during the upsampling process from the perspectives of enhancing local features and refining global features. The BGFD has effectively reduced pseudo-changes and enhanced the detection capability of detail information. It has also achieved state-of-the-art performance on four publicly available datasets – DSIFN-CD, SYSU-CD, LEVIR-CD, and S2Looking, surpassing baseline models by +8.58%, +1.28%, +0.31%, and +3.76%, respectively, in terms of the F1-Score metric. |
| format | Article |
| id | doaj-art-2d769cf09f2d49b99c457f7467af4076 |
| institution | Matheson Library |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-2d769cf09f2d49b99c457f7467af40762025-07-31T10:58:13ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-08-0112610.1080/10095020.2025.2508224Bi-temporal Gaussian feature dependency guided change detection in remote sensing imagesYi Xiao0Bin Luo1Jun Liu2Xin Su3Wei Wang4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaSchool of Artificial Intelligence, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaChange detection (CD) enables the identification of alterations between images of the same area captured at different times. However, existing CD methods still struggle to mitigate pseudo-changes resulting from domain information differences in multi-temporal images and instances of detail errors caused by the loss and contamination of detail features during the upsampling process in the network. To better alleviate these problems, we propose a bi-temporal Gaussian distribution feature-dependent (BGFD) network. Specifically, we first introduce the Gaussian noise domain disturbance (GNDD) module, which approximates the distribution using image statistical features to characterize domain information and samples noise to perturb the network for learning redundant domain information, mitigating domain information differences. Additionally, within the feature dependency facilitation (FDF) module, we integrate a novel mutual information difference loss ([Formula: see text]) and more sophisticated attention mechanisms to enhance the capabilities of the network, ensuring the acquisition of essential domain information. Subsequently, we have designed a novel detail feature compensation (DFC) module, which compensates for detail feature loss and contamination introduced during the upsampling process from the perspectives of enhancing local features and refining global features. The BGFD has effectively reduced pseudo-changes and enhanced the detection capability of detail information. It has also achieved state-of-the-art performance on four publicly available datasets – DSIFN-CD, SYSU-CD, LEVIR-CD, and S2Looking, surpassing baseline models by +8.58%, +1.28%, +0.31%, and +3.76%, respectively, in terms of the F1-Score metric.https://www.tandfonline.com/doi/10.1080/10095020.2025.2508224Change detection (CD)deep learningneural networksdomain disturbancedistribution approximationdetail feature compensation |
| spellingShingle | Yi Xiao Bin Luo Jun Liu Xin Su Wei Wang Bi-temporal Gaussian feature dependency guided change detection in remote sensing images Geo-spatial Information Science Change detection (CD) deep learning neural networks domain disturbance distribution approximation detail feature compensation |
| title | Bi-temporal Gaussian feature dependency guided change detection in remote sensing images |
| title_full | Bi-temporal Gaussian feature dependency guided change detection in remote sensing images |
| title_fullStr | Bi-temporal Gaussian feature dependency guided change detection in remote sensing images |
| title_full_unstemmed | Bi-temporal Gaussian feature dependency guided change detection in remote sensing images |
| title_short | Bi-temporal Gaussian feature dependency guided change detection in remote sensing images |
| title_sort | bi temporal gaussian feature dependency guided change detection in remote sensing images |
| topic | Change detection (CD) deep learning neural networks domain disturbance distribution approximation detail feature compensation |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2508224 |
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