FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change Detection
As a crucial approach for comprehending land surface changes, remote sensing images (RSIs) change detection methods based on deep learning have been extensively studied in recent years. Among these methods, the Siamese network-based method has demonstrated remarkable performance. However, existing a...
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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/10944583/ |
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| author | Fenglei Chen Haijun Liu Zhihong Zeng Xiaoheng Tan |
| author_facet | Fenglei Chen Haijun Liu Zhihong Zeng Xiaoheng Tan |
| author_sort | Fenglei Chen |
| collection | DOAJ |
| description | As a crucial approach for comprehending land surface changes, remote sensing images (RSIs) change detection methods based on deep learning have been extensively studied in recent years. Among these methods, the Siamese network-based method has demonstrated remarkable performance. However, existing approaches are susceptible to interference from irrelevant factors such as shadows, noise, and unrelated changes in a single temporal, which limits the model's ability to accurately perceive bitemporal crucial semantic information. To address the abovementioned problems, this article proposes a frequency-domain information assisted semantic interaction network (FASI-Net) to ensure the effective extraction of crucial semantic information in the original bitemporal features. We attempt to employ frequency-domain information to explicitly interact bitemporal low-frequency semantic information during the encoding process, subsequently preserving and enhancing the high-frequency information of each temporal feature. This enables model comprehensive perception of bitemporal features, effectively distinguishing the semantic similar information from the semantic difference information, and accurately identifying changed regions versus unchanged regions. Extensive experiments on three typical RSIs change detection datasets demonstrate a significant improvement in the performance of our proposed method (with 4.53%/7.57%, 6.66%/10.69%, and 5.11%/7.24% improvements over the baseline in terms of F1/intersection over union (IoU) metrics for WHU-CD, LEVIR-CD, and SYSU-CD datasets). Moreover, our FASI-Net comprehensively achieves state-of-the-art results with F1/IoU reaching 92.86%/86.67%, 91.73%/84.71%, and 83.72%/72.00% on WHU-CD, LEVIR-CD, and SYSU-CD datasets, respectively. In addition, the proposed bitemporal low-frequency semantic interaction module can be seamlessly inserted into existing change detection models to achieve effective crucial semantic information extraction. |
| format | Article |
| id | doaj-art-ac48c1431e9e410593d3c477dea6efaf |
| institution | OA Journals |
| 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-ac48c1431e9e410593d3c477dea6efaf2025-08-20T02:25:03ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118103601037310.1109/JSTARS.2025.355553610944583FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change DetectionFenglei Chen0https://orcid.org/0000-0002-5540-6070Haijun Liu1https://orcid.org/0000-0001-5782-4543Zhihong Zeng2https://orcid.org/0000-0001-7852-9172Xiaoheng Tan3https://orcid.org/0000-0001-9376-4920School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaAs a crucial approach for comprehending land surface changes, remote sensing images (RSIs) change detection methods based on deep learning have been extensively studied in recent years. Among these methods, the Siamese network-based method has demonstrated remarkable performance. However, existing approaches are susceptible to interference from irrelevant factors such as shadows, noise, and unrelated changes in a single temporal, which limits the model's ability to accurately perceive bitemporal crucial semantic information. To address the abovementioned problems, this article proposes a frequency-domain information assisted semantic interaction network (FASI-Net) to ensure the effective extraction of crucial semantic information in the original bitemporal features. We attempt to employ frequency-domain information to explicitly interact bitemporal low-frequency semantic information during the encoding process, subsequently preserving and enhancing the high-frequency information of each temporal feature. This enables model comprehensive perception of bitemporal features, effectively distinguishing the semantic similar information from the semantic difference information, and accurately identifying changed regions versus unchanged regions. Extensive experiments on three typical RSIs change detection datasets demonstrate a significant improvement in the performance of our proposed method (with 4.53%/7.57%, 6.66%/10.69%, and 5.11%/7.24% improvements over the baseline in terms of F1/intersection over union (IoU) metrics for WHU-CD, LEVIR-CD, and SYSU-CD datasets). Moreover, our FASI-Net comprehensively achieves state-of-the-art results with F1/IoU reaching 92.86%/86.67%, 91.73%/84.71%, and 83.72%/72.00% on WHU-CD, LEVIR-CD, and SYSU-CD datasets, respectively. In addition, the proposed bitemporal low-frequency semantic interaction module can be seamlessly inserted into existing change detection models to achieve effective crucial semantic information extraction.https://ieeexplore.ieee.org/document/10944583/Bitemporal crucial semantic informationchange detectionfrequency domainremote sensingwavelet transform |
| spellingShingle | Fenglei Chen Haijun Liu Zhihong Zeng Xiaoheng Tan FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Bitemporal crucial semantic information change detection frequency domain remote sensing wavelet transform |
| title | FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change Detection |
| title_full | FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change Detection |
| title_fullStr | FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change Detection |
| title_full_unstemmed | FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change Detection |
| title_short | FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change Detection |
| title_sort | fasi net frequency domain information assisted semantic interaction network for bitemporal remote sensing images change detection |
| topic | Bitemporal crucial semantic information change detection frequency domain remote sensing wavelet transform |
| url | https://ieeexplore.ieee.org/document/10944583/ |
| work_keys_str_mv | AT fengleichen fasinetfrequencydomaininformationassistedsemanticinteractionnetworkforbitemporalremotesensingimageschangedetection AT haijunliu fasinetfrequencydomaininformationassistedsemanticinteractionnetworkforbitemporalremotesensingimageschangedetection AT zhihongzeng fasinetfrequencydomaininformationassistedsemanticinteractionnetworkforbitemporalremotesensingimageschangedetection AT xiaohengtan fasinetfrequencydomaininformationassistedsemanticinteractionnetworkforbitemporalremotesensingimageschangedetection |