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 3 不同模型在SAMRS SOTA数据集上的分割结果对比 |
Tab.3 Comparison of segmentation results of different models in SAMRS SOTA dataset |
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模型 | IoU/% | OA/% | MIoU/% | 大车 | 游泳池 | 飞机 | 小车 | FCN[23] | 72.28 | 68.57 | 80.53 | 80.31 | 84.85 | 75.42 | DANet[6] | 70.54 | 77.65 | 72.14 | 71.24 | 82.14 | 72.89 | HRNet[24] | 77.61 | 79.78 | 83.28 | 83.12 | 85.45 | 75.16 | DeepLabV3[25] | 83.20 | 82.69 | 91.12 | 87.37 | 93.37 | 86.09 | Segformer[27] | 73.49 | 85.79 | 74.81 | 76.24 | 87.26 | 77.58 | UNet[26] | 75.61 | 74.34 | 80.37 | 83.08 | 87.75 | 78.35 | TransUNet[28] | 79.24 | 81.07 | 91.38 | 83.98 | 91.59 | 83.91 | SwinUNet[29] | 64.92 | 77.92 | 64.42 | 66.90 | 78.77 | 68.54 | 本研究 | 87.05 | 84.42 | 92.98 | 90.78 | 94.98 | 88.81 |
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