Cross-modal feature interaction network for heterogeneous change detection
Heterogeneous change detection is a task of considerable practical importance and significant challenge in remote sensing. Heterogeneous change detection involves identifying change areas using remote sensing images obtained from different sensors or imaging conditions. Recently, research has focuse...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2446307 |
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| _version_ | 1841553278284857344 |
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| author | Zhiwei Yang Xiaoqin Wang Haihan Lin Mengmeng Li Mengjing Lin |
| author_facet | Zhiwei Yang Xiaoqin Wang Haihan Lin Mengmeng Li Mengjing Lin |
| author_sort | Zhiwei Yang |
| collection | DOAJ |
| description | Heterogeneous change detection is a task of considerable practical importance and significant challenge in remote sensing. Heterogeneous change detection involves identifying change areas using remote sensing images obtained from different sensors or imaging conditions. Recently, research has focused on feature space translation methods based on deep learning technology for heterogeneous images. However, these types of methods often lead to the loss of original image information, and the translated features cannot be efficiently compared, further limiting the accuracy of change detection. For these issues, we propose a cross-modal feature interaction network (CMFINet). Specifically, CMFINet introduces a cross-modal interaction module (CMIM), which facilitates the interaction between heterogeneous features through attention exchange. This approach promotes consistent representation of heterogeneous features while preserving image characteristics. Additionally, we design a differential feature extraction module (DFEM) to enhance the extraction of true change features from spatial and channel dimensions, facilitating efficient comparison after feature interaction. Extensive experiments conducted on the California, Toulouse, and Wuhan datasets demonstrate that CMFINet outperforms eight existing methods in identifying change areas in different scenes from multimodal images. Compared to the existing methods applied to the three datasets, CMFINet achieved the highest F1 scores of 83.93%, 75.65%, and 95.42%, and the highest mIoU values of 85.38%, 78.34%, and 94.87%, respectively. The results demonstrate the effectiveness and applicability of CMFINet in heterogeneous change detection. |
| format | Article |
| id | doaj-art-0e2c516b002e42019a19437ef2761210 |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-0e2c516b002e42019a19437ef27612102025-01-09T10:45:03ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-01-0112210.1080/10095020.2024.2446307Cross-modal feature interaction network for heterogeneous change detectionZhiwei Yang0Xiaoqin Wang1Haihan Lin2Mengmeng Li3Mengjing Lin4Key Lab of Spatia Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, ChinaKey Lab of Spatia Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, ChinaKey Lab of Spatia Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, ChinaKey Lab of Spatia Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, ChinaKey Lab of Spatia Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, ChinaHeterogeneous change detection is a task of considerable practical importance and significant challenge in remote sensing. Heterogeneous change detection involves identifying change areas using remote sensing images obtained from different sensors or imaging conditions. Recently, research has focused on feature space translation methods based on deep learning technology for heterogeneous images. However, these types of methods often lead to the loss of original image information, and the translated features cannot be efficiently compared, further limiting the accuracy of change detection. For these issues, we propose a cross-modal feature interaction network (CMFINet). Specifically, CMFINet introduces a cross-modal interaction module (CMIM), which facilitates the interaction between heterogeneous features through attention exchange. This approach promotes consistent representation of heterogeneous features while preserving image characteristics. Additionally, we design a differential feature extraction module (DFEM) to enhance the extraction of true change features from spatial and channel dimensions, facilitating efficient comparison after feature interaction. Extensive experiments conducted on the California, Toulouse, and Wuhan datasets demonstrate that CMFINet outperforms eight existing methods in identifying change areas in different scenes from multimodal images. Compared to the existing methods applied to the three datasets, CMFINet achieved the highest F1 scores of 83.93%, 75.65%, and 95.42%, and the highest mIoU values of 85.38%, 78.34%, and 94.87%, respectively. The results demonstrate the effectiveness and applicability of CMFINet in heterogeneous change detection.https://www.tandfonline.com/doi/10.1080/10095020.2024.2446307Change detectionheterogeneous remote sensing imagesfeature interactionCNNattention mechanisms |
| spellingShingle | Zhiwei Yang Xiaoqin Wang Haihan Lin Mengmeng Li Mengjing Lin Cross-modal feature interaction network for heterogeneous change detection Geo-spatial Information Science Change detection heterogeneous remote sensing images feature interaction CNN attention mechanisms |
| title | Cross-modal feature interaction network for heterogeneous change detection |
| title_full | Cross-modal feature interaction network for heterogeneous change detection |
| title_fullStr | Cross-modal feature interaction network for heterogeneous change detection |
| title_full_unstemmed | Cross-modal feature interaction network for heterogeneous change detection |
| title_short | Cross-modal feature interaction network for heterogeneous change detection |
| title_sort | cross modal feature interaction network for heterogeneous change detection |
| topic | Change detection heterogeneous remote sensing images feature interaction CNN attention mechanisms |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2024.2446307 |
| work_keys_str_mv | AT zhiweiyang crossmodalfeatureinteractionnetworkforheterogeneouschangedetection AT xiaoqinwang crossmodalfeatureinteractionnetworkforheterogeneouschangedetection AT haihanlin crossmodalfeatureinteractionnetworkforheterogeneouschangedetection AT mengmengli crossmodalfeatureinteractionnetworkforheterogeneouschangedetection AT mengjinglin crossmodalfeatureinteractionnetworkforheterogeneouschangedetection |