基于CNN和Efficient Transformer的多尺度遥感图像语义分割算法

张振利,胡新凯,李凡,冯志成,陈智超

Semantic segmentation algorithm for multiscale remote sensing images based on CNN and Efficient Transformer

Zhenli ZHANG,Xinkai HU,Fan LI,Zhicheng FENG,Zhichao CHEN

表 2 不同模型在ISPRS Postdam数据集上的分割结果对比

Tab.2 Comparison of segmentation results of different models in ISPRS Postdam dataset

模型 IoU/% OA/% MIoU/% 不透明表面 建筑物 低矮植被 树木 汽车 FCN[23] 76.31 83.23 64.65 66.03 68.78 86.04 71.80 DANet[6] 77.34 82.52 64.73 70.78 79.87 86.94 75.05 HRNet[24] 79.11 84.97 67.95 70.53 81.65 87.78 76.84 DeepLabV3[25] 78.90 85.23 68.68 70.91 83.17 87.73 77.38 Segformer[27] 79.96 86.70 69.72 65.21 77.64 87.09 75.85 UNet[26] 76.86 83.74 65.90 63.69 79.13 86.01 73.86 TransUNet[28] 79.79 86.13 68.94 66.30 78.63 86.41 75.96 SwinUNet[29] 73.01 76.29 61.74 54.27 68.88 80.49 66.83 本研究 86.05 92.60 74.93 73.68 84.17 90.50 82.29