FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images
Change detection is an important technique that identifies areas of change by comparing images of the same location taken at different times, and it is widely used in urban expansion monitoring, resource exploration, land use detection, and post-disaster monitoring. However, existing change detectio...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/5/824 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850051809331118080 |
|---|---|
| author | Bochao Chen Yapeng Wang Xu Yang Xiaochen Yuan Sio Kei Im |
| author_facet | Bochao Chen Yapeng Wang Xu Yang Xiaochen Yuan Sio Kei Im |
| author_sort | Bochao Chen |
| collection | DOAJ |
| description | Change detection is an important technique that identifies areas of change by comparing images of the same location taken at different times, and it is widely used in urban expansion monitoring, resource exploration, land use detection, and post-disaster monitoring. However, existing change detection methods often struggle with balancing the extraction of fine-grained spatial details and effective semantic information integration, particularly for high-resolution remote sensing imagery. This paper proposes a high-resolution remote sensing image change detection model called FFLKCDNet (First Fusion Large-Kernel Change Detection Network) to solve this issue. FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. The model also includes a Contextual Dual-Land-Cover Attention Fusion Module (CD-LKAFM) to integrate multi-scale information during the feature recovery stage, improving the resolution of details and the integration of semantic information. Experimental results showed that FFLKCDNet outperformed existing methods on datasets such as GVLM, SYSU, and LEVIR, achieving superior performance in metrics such as Kappa coefficient, mIoU, MPA, and F1 score. The model achieves high-precision change detection for remote sensing images through multi-scale feature fusion, noise suppression, and fine-grained information capture. These advancements pave the way for more precise and reliable applications in urban planning, environmental monitoring, and disaster management. |
| format | Article |
| id | doaj-art-61c4d1863b194fe59cfb82d3dd1e9a2d |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-61c4d1863b194fe59cfb82d3dd1e9a2d2025-08-20T02:53:02ZengMDPI AGRemote Sensing2072-42922025-02-0117582410.3390/rs17050824FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing ImagesBochao Chen0Yapeng Wang1Xu Yang2Xiaochen Yuan3Sio Kei Im4Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaMacao Polytechnic University, Macao 999078, ChinaChange detection is an important technique that identifies areas of change by comparing images of the same location taken at different times, and it is widely used in urban expansion monitoring, resource exploration, land use detection, and post-disaster monitoring. However, existing change detection methods often struggle with balancing the extraction of fine-grained spatial details and effective semantic information integration, particularly for high-resolution remote sensing imagery. This paper proposes a high-resolution remote sensing image change detection model called FFLKCDNet (First Fusion Large-Kernel Change Detection Network) to solve this issue. FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. The model also includes a Contextual Dual-Land-Cover Attention Fusion Module (CD-LKAFM) to integrate multi-scale information during the feature recovery stage, improving the resolution of details and the integration of semantic information. Experimental results showed that FFLKCDNet outperformed existing methods on datasets such as GVLM, SYSU, and LEVIR, achieving superior performance in metrics such as Kappa coefficient, mIoU, MPA, and F1 score. The model achieves high-precision change detection for remote sensing images through multi-scale feature fusion, noise suppression, and fine-grained information capture. These advancements pave the way for more precise and reliable applications in urban planning, environmental monitoring, and disaster management.https://www.mdpi.com/2072-4292/17/5/824remote sensing change detectionhigh-resolution imagesFFLKCDNetlarge-kernel convolutionmulti-scale feature fusionRAResNet |
| spellingShingle | Bochao Chen Yapeng Wang Xu Yang Xiaochen Yuan Sio Kei Im FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images Remote Sensing remote sensing change detection high-resolution images FFLKCDNet large-kernel convolution multi-scale feature fusion RAResNet |
| title | FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images |
| title_full | FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images |
| title_fullStr | FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images |
| title_full_unstemmed | FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images |
| title_short | FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images |
| title_sort | fflkcdnet first fusion large kernel change detection network for high resolution remote sensing images |
| topic | remote sensing change detection high-resolution images FFLKCDNet large-kernel convolution multi-scale feature fusion RAResNet |
| url | https://www.mdpi.com/2072-4292/17/5/824 |
| work_keys_str_mv | AT bochaochen fflkcdnetfirstfusionlargekernelchangedetectionnetworkforhighresolutionremotesensingimages AT yapengwang fflkcdnetfirstfusionlargekernelchangedetectionnetworkforhighresolutionremotesensingimages AT xuyang fflkcdnetfirstfusionlargekernelchangedetectionnetworkforhighresolutionremotesensingimages AT xiaochenyuan fflkcdnetfirstfusionlargekernelchangedetectionnetworkforhighresolutionremotesensingimages AT siokeiim fflkcdnetfirstfusionlargekernelchangedetectionnetworkforhighresolutionremotesensingimages |