Journal of Guangdong University of Technology ›› 2022, Vol. 39 ›› Issue (01): 63-70.doi: 10.12052/gdutxb.210014

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A Two-way Network Model for Semantic Segmentation

Yang Yun-long, Liang Lu, Teng Shao-hua   

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-01-25 Published:2022-01-20

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Abstract: When deep convolutional neural networks perform semantic segmentation of high-resolution remote sensing images, the downsampling of images can cause blurring of object edges, making the segmentation results unclearly delineated near the edges and more misclassified. The segmentation ability of the model for remote sensing images can be improved by adding edge information in the network. Therefore, a two-way network model is proposed for semantic segmentation, adding one-way edge network to learn the edge features of the target and refining the segmentation features using the edge features. Meanwhile, the segmentation network and the edge network can be trained simultaneously as a multi-task learning model. In this paper, the effectiveness of the two-way network model is demonstrated on the ISPRS Potsdam and ISPRS Vaihingen dataset datasets, and leading results are achieved when comparing multiple semantic segmentation models.

Key words: two-way network, edge detection, high-resolution remote sensing images, semantic segmentation

CLC Number: 

  • TP391
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