| Citation: | LIU C J，QIAO Z，YAN H W，et al. Semantic segmentation network of remote sensing images based on dual path supervision[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（3）：732-741 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0155
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A dual path supervision and attention filtering network was proposed to solve the problem of blurry boundary classification of target objects in semantic segmentation tasks of remote sensing images. A supervised boundary extraction module was introduced to increase the channel of boundary information, improve the weight of boundary information in semantic segmentation, and enhance attention to the boundary pixels of the target object. The attention filtering module was introduced to filter out spatial details in shallow networks and abstract semantic information in deep networks through attention maps, discarding redundant information in the network to prevent overfitting. The mean intersection over union of the dual path supervision and attention filtering network on the Potsdam dataset and the Jiage dataset was 85.44% and 86.07% respectively, which increased by 1.24%, 1.28% and 1.54%, 1.27% compared with the suboptimal network MagNet and SAPNet. Experimental results show that the proposed network can more accurately segment the boundaries of target objects.
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