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 5 不同模型在SAMRS FAST数据集上的分割结果比较 |
Tab.5 Comparison of segmentation results of different models in SAMRS FAST dataset |
|
模型 | IoU/% | OA/% | MIoU/% | 棒球场 | 桥梁 | 足球场 | 汽车 | FCN[23] | 80.21 | 94.17 | 90.26 | 63.76 | 90.63 | 82.10 | DANet[6] | 84.97 | 87.29 | 91.66 | 55.01 | 87.88 | 80.23 | HRNet[24] | 92.32 | 84.83 | 91.83 | 51.51 | 88.64 | 80.12 | DeepLabV3[25] | 93.57 | 95.75 | 96.98 | 54.97 | 92.89 | 85.32 | Segformer[27] | 87.95 | 93.87 | 94.48 | 42.71 | 86.77 | 79.75 | UNet[26] | 84.30 | 92.78 | 93.53 | 59.70 | 90.21 | 82.58 | TransUNet[28] | 94.46 | 85.84 | 95.66 | 60.33 | 91.83 | 84.07 | SwinUNet[29] | 87.83 | 90.05 | 95.29 | 43.72 | 87.49 | 79.22 | 本研究 | 93.79 | 92.91 | 95.98 | 63.93 | 93.45 | 86.65 |
|
|
|