MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection
With the widespread application of convolutional neural networks (CNNs) in remote sensing (RS) technologies, change detection (CD) has attracted increasing attention in environmental monitoring research. In RS image CD tasks, researchers often face challenges such as multiscale change capture inadeq...
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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/11003578/ |
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| author | YeKai Cui Peng Duan Jinjiang Li |
| author_facet | YeKai Cui Peng Duan Jinjiang Li |
| author_sort | YeKai Cui |
| collection | DOAJ |
| description | With the widespread application of convolutional neural networks (CNNs) in remote sensing (RS) technologies, change detection (CD) has attracted increasing attention in environmental monitoring research. In RS image CD tasks, researchers often face challenges such as multiscale change capture inadequacy due to feature noise interference and difficulties in spatiotemporal feature alignment. To address these issues, this article proposes a multitemporal and spatial feature reconstruction denoising network for remote sensing change detection (MTFSR). MTFSR first utilizes ResNet50 to extract deep features from dual-temporal images and innovatively introduces Bézier curves to smooth the features through feature redistribution, along with brightness enhancement to effectively suppress noise interference and enhance change areas. Meanwhile, a spatial attention mechanism and discrete cosine transform (DCT) are incorporated to further improve the spatial expression and frequency domain perception of the features. Then, multiscale filtering is applied to accurately align the frequency and spatial domains, effectively solving the spatiotemporal feature inconsistency problem. On this basis, the model designs a dynamic attention unit that includes both local and global attention to generate comprehensive feature maps and calculates difference weights through linear fusion, further strengthening the feature expression ability to more accurately capture change information. Finally, the decoder generates high-precision CD results. To verify the effectiveness of MTFSR in remote sensing change detection tasks, we conducted tests on three challenging datasets (LEVIR-CD, GZ-CD, and WHU-CD). The experimental results show that MTFSR significantly outperforms traditional methods and most existing advanced techniques in change detection performance. |
| format | Article |
| id | doaj-art-71ad9f541fef4df2b50a5241f6d535b5 |
| institution | Kabale University |
| 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-71ad9f541fef4df2b50a5241f6d535b52025-08-20T03:29:28ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118147661478310.1109/JSTARS.2025.357010811003578MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change DetectionYeKai Cui0https://orcid.org/0009-0001-2445-4408Peng Duan1https://orcid.org/0009-0002-2333-4735Jinjiang Li2https://orcid.org/0000-0002-2080-8678School of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaWith the widespread application of convolutional neural networks (CNNs) in remote sensing (RS) technologies, change detection (CD) has attracted increasing attention in environmental monitoring research. In RS image CD tasks, researchers often face challenges such as multiscale change capture inadequacy due to feature noise interference and difficulties in spatiotemporal feature alignment. To address these issues, this article proposes a multitemporal and spatial feature reconstruction denoising network for remote sensing change detection (MTFSR). MTFSR first utilizes ResNet50 to extract deep features from dual-temporal images and innovatively introduces Bézier curves to smooth the features through feature redistribution, along with brightness enhancement to effectively suppress noise interference and enhance change areas. Meanwhile, a spatial attention mechanism and discrete cosine transform (DCT) are incorporated to further improve the spatial expression and frequency domain perception of the features. Then, multiscale filtering is applied to accurately align the frequency and spatial domains, effectively solving the spatiotemporal feature inconsistency problem. On this basis, the model designs a dynamic attention unit that includes both local and global attention to generate comprehensive feature maps and calculates difference weights through linear fusion, further strengthening the feature expression ability to more accurately capture change information. Finally, the decoder generates high-precision CD results. To verify the effectiveness of MTFSR in remote sensing change detection tasks, we conducted tests on three challenging datasets (LEVIR-CD, GZ-CD, and WHU-CD). The experimental results show that MTFSR significantly outperforms traditional methods and most existing advanced techniques in change detection performance.https://ieeexplore.ieee.org/document/11003578/Attention mechanism transformerbuilding change detection (BCD)change detectionhigh-resolution remote sensing (RS) imagesimage fusion |
| spellingShingle | YeKai Cui Peng Duan Jinjiang Li MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Attention mechanism transformer building change detection (BCD) change detection high-resolution remote sensing (RS) images image fusion |
| title | MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection |
| title_full | MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection |
| title_fullStr | MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection |
| title_full_unstemmed | MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection |
| title_short | MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection |
| title_sort | mtfsr multitemporal and spatial feature reconstruction denoising network for remote sensing change detection |
| topic | Attention mechanism transformer building change detection (BCD) change detection high-resolution remote sensing (RS) images image fusion |
| url | https://ieeexplore.ieee.org/document/11003578/ |
| work_keys_str_mv | AT yekaicui mtfsrmultitemporalandspatialfeaturereconstructiondenoisingnetworkforremotesensingchangedetection AT pengduan mtfsrmultitemporalandspatialfeaturereconstructiondenoisingnetworkforremotesensingchangedetection AT jinjiangli mtfsrmultitemporalandspatialfeaturereconstructiondenoisingnetworkforremotesensingchangedetection |