基于CNN和Efficient Transformer的多尺度遥感图像语义分割算法
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张振利,胡新凯,李凡,冯志成,陈智超
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Semantic segmentation algorithm for multiscale remote sensing images based on CNN and Efficient Transformer
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Zhenli ZHANG,Xinkai HU,Fan LI,Zhicheng FENG,Zhichao CHEN
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表 4 不同模型在SAMRS SIOR数据集上的分割结果对比 |
Tab.4 Comparison of segmentation results of different models in SAMRS SIOR dataset |
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模型 | IoU/% | OA/% | MIoU/% | 飞机 | 棒球场 | 轮船 | 网球场 | FCN[23] | 82.29 | 95.61 | 95.41 | 95.59 | 96.97 | 92.22 | DANet[6] | 78.32 | 96.11 | 96.03 | 96.36 | 94.59 | 91.71 | HRNet[24] | 83.01 | 93.62 | 94.74 | 95.67 | 96.47 | 91.76 | DeepLabV3[25] | 90.34 | 96.10 | 97.69 | 95.34 | 97.83 | 94.87 | Segformer[27] | 73.65 | 94.10 | 95.19 | 91.43 | 92.65 | 88.59 | UNet[26] | 77.38 | 92.03 | 92.34 | 96.62 | 93.47 | 89.59 | TransUNet[28] | 92.76 | 96.45 | 97.18 | 97.48 | 97.88 | 95.97 | SwinUNet[29] | 80.88 | 95.43 | 92.65 | 93.85 | 94.49 | 90.70 | 本研究 | 94.38 | 98.65 | 97.77 | 98.38 | 98.93 | 97.29 |
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