DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation Network
Remote sensing image segmentation holds significant application value in precision agriculture, environmental monitoring, and other fields. However, in the task of fine-grained segmentation of weeds and crops, traditional deep learning methods often fail to balance global semantic information with l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2203 |
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| author | Xiangfei She Zhankui Tang Xin Pan Jian Zhao Wenyu Liu |
| author_facet | Xiangfei She Zhankui Tang Xin Pan Jian Zhao Wenyu Liu |
| author_sort | Xiangfei She |
| collection | DOAJ |
| description | Remote sensing image segmentation holds significant application value in precision agriculture, environmental monitoring, and other fields. However, in the task of fine-grained segmentation of weeds and crops, traditional deep learning methods often fail to balance global semantic information with local detail features, resulting in over-segmentation or under-segmentation issues. To address this challenge, this paper proposes a segmentation model based on a dual-branch Transformer architecture—DBFormer—to enhance the accuracy of weed detection in remote sensing images. This approach integrates the following techniques: (1) a dynamic context aggregation branch (DCA-Branch) with adaptive downsampling attention to model long-range dependencies and suppress background noise, and (2) a local detail enhancement branch (LDE-Branch) leveraging depthwise-separable convolutions with residual refinement to preserve and sharpen small weed edges. An Edge-Aware Loss module further reinforces boundary clarity. On the Tobacco Dataset, DBFormer achieves an mIoU of 86.48%, outperforming the best baseline by 3.83%; on the Sunflower Dataset, it reaches 85.49% mIoU, a 4.43% absolute gain. These results demonstrate that our dual-branch synergy effectively resolves the global–local conflict, delivering superior accuracy and stability in the context of practical agricultural applications. |
| format | Article |
| id | doaj-art-dbb1acd7add2441688056b25bb10eb91 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-dbb1acd7add2441688056b25bb10eb912025-08-20T03:50:21ZengMDPI AGRemote Sensing2072-42922025-06-011713220310.3390/rs17132203DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation NetworkXiangfei She0Zhankui Tang1Xin Pan2Jian Zhao3Wenyu Liu4School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Foreign Languages and Cultures, Jilin University, Changchun 130012, ChinaRemote sensing image segmentation holds significant application value in precision agriculture, environmental monitoring, and other fields. However, in the task of fine-grained segmentation of weeds and crops, traditional deep learning methods often fail to balance global semantic information with local detail features, resulting in over-segmentation or under-segmentation issues. To address this challenge, this paper proposes a segmentation model based on a dual-branch Transformer architecture—DBFormer—to enhance the accuracy of weed detection in remote sensing images. This approach integrates the following techniques: (1) a dynamic context aggregation branch (DCA-Branch) with adaptive downsampling attention to model long-range dependencies and suppress background noise, and (2) a local detail enhancement branch (LDE-Branch) leveraging depthwise-separable convolutions with residual refinement to preserve and sharpen small weed edges. An Edge-Aware Loss module further reinforces boundary clarity. On the Tobacco Dataset, DBFormer achieves an mIoU of 86.48%, outperforming the best baseline by 3.83%; on the Sunflower Dataset, it reaches 85.49% mIoU, a 4.43% absolute gain. These results demonstrate that our dual-branch synergy effectively resolves the global–local conflict, delivering superior accuracy and stability in the context of practical agricultural applications.https://www.mdpi.com/2072-4292/17/13/2203precision agriculturefine-grained weed segmentationdual-branch Transformer architecturedynamic context aggregationlocal detail enhancementedge-aware loss |
| spellingShingle | Xiangfei She Zhankui Tang Xin Pan Jian Zhao Wenyu Liu DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation Network Remote Sensing precision agriculture fine-grained weed segmentation dual-branch Transformer architecture dynamic context aggregation local detail enhancement edge-aware loss |
| title | DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation Network |
| title_full | DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation Network |
| title_fullStr | DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation Network |
| title_full_unstemmed | DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation Network |
| title_short | DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation Network |
| title_sort | dbformer a dual branch adaptive remote sensing image resolution fine grained weed segmentation network |
| topic | precision agriculture fine-grained weed segmentation dual-branch Transformer architecture dynamic context aggregation local detail enhancement edge-aware loss |
| url | https://www.mdpi.com/2072-4292/17/13/2203 |
| work_keys_str_mv | AT xiangfeishe dbformeradualbranchadaptiveremotesensingimageresolutionfinegrainedweedsegmentationnetwork AT zhankuitang dbformeradualbranchadaptiveremotesensingimageresolutionfinegrainedweedsegmentationnetwork AT xinpan dbformeradualbranchadaptiveremotesensingimageresolutionfinegrainedweedsegmentationnetwork AT jianzhao dbformeradualbranchadaptiveremotesensingimageresolutionfinegrainedweedsegmentationnetwork AT wenyuliu dbformeradualbranchadaptiveremotesensingimageresolutionfinegrainedweedsegmentationnetwork |