Remote sensing semantic segmentation method based on high-resolution relational graph convolutional network

WANG Yinda; CHEN Jiahui; PENG Ling; LI Zhaobo; YANG Lina

doi:10.7523/j.ucas.2023.079

Journal of University of Chinese Academy of Sciences >

2025 , Vol. 42 >Issue 1: 107 - 115

DOI: https://doi.org/10.7523/j.ucas.2023.079

Research Articles

Remote sensing semantic segmentation method based on high-resolution relational graph convolutional network

WANG Yinda ,
CHEN Jiahui ,
PENG Ling ,
LI Zhaobo ,
YANG Lina

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1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2023-07-07

Revised date: 2023-10-07

Online published: 2023-10-07

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Abstract

Semantic segmentation of remote sensing images is an important task in remote sensing image processing and analysis, especially in multi-category semantic segmentation. Current methods mainly revolve around convolutional neural networks, but convolution only focuses on the local information of the image while ignoring the global information. Therefore, inspired by high resolution network (HRNet) and relational graph convolutional network (R-GCN), this paper proposes a high-resolution relational graph convolutional network (HRGCN) for multi-category semantic segmentation. Firstly, simple linear iterative clustering (SLIC) is done on the original image, and the result is used to segment the feature map output from HRNet to obtain superpixel blocks with high homogeneity and containing multi-resolution information; then graph nodes and edges are constructed based on the superpixel blocks, and R-GCN is used to classify the graph nodes, so as to learn the long-distance dependency between different features and complete the extraction and classification of remote sensing images. The HRGCN model designed in this paper is experimented on Potsdam and Vaihingen datasets, and the results are compared with the existing methods, and the $\bar{F}_1$ values and MIoU values are improved to certain degrees, which proves that the method has good advancement.

Key words： remote sensing image; R-GCN; HRNet; superpixel; semamtic segmentation; image processing

Cite this article

WANG Yinda , CHEN Jiahui , PENG Ling , LI Zhaobo , YANG Lina . Remote sensing semantic segmentation method based on high-resolution relational graph convolutional network[J]. Journal of University of Chinese Academy of Sciences, 2025 , 42(1) : 107 -115 . DOI: 10.7523/j.ucas.2023.079

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