Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction Branch
Due to the combined effects of complex spectral mixtures, blurred boundaries of ground objects, and environmental noise, it is extremely challenging to accurately identify water boundaries from high-resolution remote sensing images. To address this problem, this paper proposes a water body extractio...
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2025-05-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2404085.pdf |
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| author | ZHOU Ke, CHANG Ranran, XU Xizhi, MIAO Ru, ZHANG Guangyu, WANG Jiaqian |
| author_facet | ZHOU Ke, CHANG Ranran, XU Xizhi, MIAO Ru, ZHANG Guangyu, WANG Jiaqian |
| author_sort | ZHOU Ke, CHANG Ranran, XU Xizhi, MIAO Ru, ZHANG Guangyu, WANG Jiaqian |
| collection | DOAJ |
| description | Due to the combined effects of complex spectral mixtures, blurred boundaries of ground objects, and environmental noise, it is extremely challenging to accurately identify water boundaries from high-resolution remote sensing images. To address this problem, this paper proposes a water body extraction method based on ConvNeXt and dual feature extraction branch (CoNFM-Net) on the basis of PSPNet. In the encoder stage, ConvNeXt is used instead of ResNet50 as the backbone network, which uses inverted bottleneck layer, large kernel and other designs to enhance the feature extraction ability of the network. In the decoder stage, a dual feature extraction branch structure with multi-scale feature fusion and context information enhancement is designed. In order to effectively utilize the multi-level feature map generated by the backbone network, a bidirectional feature fusion module (BiFFM) is designed to solve the problem of scale inconsistency in boundary recognition. Aiming to improve the utilization rate of global information, the deep feature map output by the backbone network is passed through the global context information module (GCIM). At the same time, the deepest feature map of the multi-scale feature fusion branch is spliced with it to enhance the model’s ability to capture the details of the water boundary. Experimental results show that the mean intersection over union and F1-score of this method on LoveDA dataset, GF-2 dataset and Sentinel-2 dataset are 89.64%, 94.32%, 92.60%, 96.16% and 93.72%, 96.73%, respectively. In the same environment, compared with U-Net, DANet, CMTFNet and other semantic segmentation algorithms, the proposed algorithm CoNFM-Net has certain advantages. |
| format | Article |
| id | doaj-art-52f45571fe0b4e1e88dfe4d1757262f9 |
| institution | OA Journals |
| issn | 1673-9418 |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj-art-52f45571fe0b4e1e88dfe4d1757262f92025-08-20T02:27:10ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182025-05-011951264127910.3778/j.issn.1673-9418.2404085Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction BranchZHOU Ke, CHANG Ranran, XU Xizhi, MIAO Ru, ZHANG Guangyu, WANG Jiaqian01. School of Computer and Information Engineering, Henan University, Kaifeng, Henan 475004, China 2. Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng, Henan 475004, China 3. Henan Provincial Spatio-Temporal Big Data Technology Innovation Center, Zhengzhou 450046, China 4. Department of Information Business, Henan Jiuyu Tenglong Information Engineering Co., Ltd., Zhengzhou 450052, ChinaDue to the combined effects of complex spectral mixtures, blurred boundaries of ground objects, and environmental noise, it is extremely challenging to accurately identify water boundaries from high-resolution remote sensing images. To address this problem, this paper proposes a water body extraction method based on ConvNeXt and dual feature extraction branch (CoNFM-Net) on the basis of PSPNet. In the encoder stage, ConvNeXt is used instead of ResNet50 as the backbone network, which uses inverted bottleneck layer, large kernel and other designs to enhance the feature extraction ability of the network. In the decoder stage, a dual feature extraction branch structure with multi-scale feature fusion and context information enhancement is designed. In order to effectively utilize the multi-level feature map generated by the backbone network, a bidirectional feature fusion module (BiFFM) is designed to solve the problem of scale inconsistency in boundary recognition. Aiming to improve the utilization rate of global information, the deep feature map output by the backbone network is passed through the global context information module (GCIM). At the same time, the deepest feature map of the multi-scale feature fusion branch is spliced with it to enhance the model’s ability to capture the details of the water boundary. Experimental results show that the mean intersection over union and F1-score of this method on LoveDA dataset, GF-2 dataset and Sentinel-2 dataset are 89.64%, 94.32%, 92.60%, 96.16% and 93.72%, 96.73%, respectively. In the same environment, compared with U-Net, DANet, CMTFNet and other semantic segmentation algorithms, the proposed algorithm CoNFM-Net has certain advantages.http://fcst.ceaj.org/fileup/1673-9418/PDF/2404085.pdfwater body extraction; convnext; high-resolution remote sensing images; feature fusion; dual feature extraction branch |
| spellingShingle | ZHOU Ke, CHANG Ranran, XU Xizhi, MIAO Ru, ZHANG Guangyu, WANG Jiaqian Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction Branch Jisuanji kexue yu tansuo water body extraction; convnext; high-resolution remote sensing images; feature fusion; dual feature extraction branch |
| title | Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction Branch |
| title_full | Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction Branch |
| title_fullStr | Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction Branch |
| title_full_unstemmed | Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction Branch |
| title_short | Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction Branch |
| title_sort | water body extraction method based on convnext and dual feature extraction branch |
| topic | water body extraction; convnext; high-resolution remote sensing images; feature fusion; dual feature extraction branch |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2404085.pdf |
| work_keys_str_mv | AT zhoukechangranranxuxizhimiaoruzhangguangyuwangjiaqian waterbodyextractionmethodbasedonconvnextanddualfeatureextractionbranch |