Opto-Electronic Engineering, Volume. 52, Issue 1, 240236(2025)
Remote sensing image road extraction by integrating ResNeSt and multi-scale feature fusion
Figures & Tables(15)
Fig. 9. Road extraction results of different models (Massachusetts)
Table 1. Comparison of experimental results of different improvements (Massachusetts)
View tableView in ArticleTable 1. Comparison of experimental results of different improvements (Massachusetts)
Method OA/% F1/% IoU/% mIoU/% U-Net 97.71 87.46 62.37 80.01 ResNeSt block+U-Net 97.79 87.51 62.79 80.36 Att+U-Net 97.76 87.49 62.41 80.15 Transition+U-Net 97.73 87.48 62.39 80.10 U-Net+MSFF 97.85 87.67 62.76 80.59 ResNeSt block+Att+U-Net 97.89 87.83 63.11 80.68 ResNeSt block+Att+Transition+U-Net 97.91 87.91 63.57 80.74 ResNeSt block+Att+Transition+U-Net+MSFF 98.09 88.83 64.76 81.94
Table 2. Comparison of experimental results of different improvements (DeepGlobe)
View tableView in ArticleTable 2. Comparison of experimental results of different improvements (DeepGlobe)
Method OA/% F1/% IoU/% mIoU/% U-Net 97.07 86.12 61.98 79.82 ResNeSt block+U-Net 97.13 86.27 62.08 80.01 Att+U-Net 97.32 86.41 62.34 80.13 Transition+U-Net 97.11 86.03 61.13 79.69 U-Net+MSFF 97.73 86.91 62.60 80.61 ResNeSt block+Att+U-Net 97.81 87.29 63.07 80.94 ResNeSt block+Att+Transition+U-Net 97.93 87.55 63.41 81.01 ResNeSt block+Att+Transition+U-Net+MSFF 98.05 88.01 64.45 81.35
Table 3. Comparison of road extraction results from different networks (Massachusetts)
View tableView in ArticleTable 3. Comparison of road extraction results from different networks (Massachusetts)
Method OA/% F1/% IoU/% mIoU/% U-Net 97.71 87.46 62.37 80.01 DDUNet 97.62 87.13 61.67 79.63 ResUNet 97.72 87.88 62.43 80.16 DeepLabV3+ 97.62 87.48 61.62 79.71 D-LinkNet 97.83 87.91 63.11 80.83 U2-Net 97.65 87.16 61.85 79.65 MINet 97.88 88.10 63.34 80.37 ResT-UNet 98.09 88.83 64.76 81.94
Table 4. Comparison of road extraction results from different networks (DeepGlobe)
View tableView in ArticleTable 4. Comparison of road extraction results from different networks (DeepGlobe)
Method OA/% F1/% IoU/% mIoU/% U-Net 97.07 86.12 61.98 79.82 DDUNet 97.23 86.87 62.18 80.12 ResUNet 97.11 86.28 62.10 79.97 DeepLabV3+ 96.13 85.84 61.02 78.94 D-LinkNet 97.52 86.57 63.07 80.76 U2-Net 97.43 86.18 62.84 80.56 MINet 97.48 86.88 62.71 80.41 ResT-UNet 98.05 88.01 64.45 81.35
Table 5. Complexity of different algorithms
View tableView in ArticleTable 5. Complexity of different algorithms
Algorithm U-Net DeepLabv3+ D-LinkNet ResUNet U2-Net DDUNet MINet ResT-UNet FLOPs/G 25947.00 26474.89 120408.56 41837.93 157679.07 77489.09 384409.60 54994.39 Params/M 24.73 5.83 213.87 22.60 44.17 65.84 47.56 51.13 Latency/s 3.78 3.49 4.79 3.71 5.00 4.13 6.45 3.57
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Ming Hao, He Bai, Tingting Xu. Remote sensing image road extraction by integrating ResNeSt and multi-scale feature fusion[J]. Opto-Electronic Engineering, 2025, 52(1): 240236
Paper Information
Category: Article
Received: Oct. 9, 2024
Accepted: Dec. 16, 2024
Published Online: Feb. 21, 2025
The Author Email: Ming Hao (郝明)
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