基于多尺度互注意力的遥感图像语义分割网络

刘春娟,乔泽,闫浩文,吴小所,王嘉伟,辛钰强

Semantic segmentation network for remote sensing image based on multi-scale mutual attention

Chun-juan LIU,Ze QIAO,Hao-wen YAN,Xiao-suo WU,Jia-wei WANG,Yu-qiang XIN

表 6 在Potsdam数据集上与8种最先进的方法进行定量比较

Tab.6 Quantitative comparison with 8 state-of-the-art methods on Potsdam dataset

模型 IoU/% mIoU/% 背景 汽车 不透水表面 树 低植被 建筑物 SegNet 69.49 59.85 83.44 52.97 79.26 80.36 70.90 PSPNet 78.33 65.84 86.78 56.21 81.55 88.32 76.17 DeeplabV3 78.86 67.57 85.63 60.38 80.57 87.51 76.75 MSRF 77.22 73.86 85.56 73.40 79.60 90.66 80.05 EMANet 77.40 75.60 85.60 80.70 82.10 89.30 81.80 CCNet 76.39 78.79 87.60 79.62 82.24 89.71 82.39 DANNet 82.19 77.35 87.28 82.57 82.62 92.51 84.09 MagNet 79.54 82.09 88.67 79.85 83.00 92.07 84.20 DCED-MMA-CGU 83.21 82.42 87.89 83.09 83.79 92.71 85.52