HRNet-based extraction of building change information from high-resolution remote sensing images

doi:10.13474/j.cnki.11-2246.2022.0153

Bulletin of Surveying and Mapping ›› 2022, Vol. 0 ›› Issue (5): 126-132.doi: 10.13474/j.cnki.11-2246.2022.0153

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HRNet-based extraction of building change information from high-resolution remote sensing images

CHEN Zhilang^1,2,3, FU Zhenhua^1,2,3, ZHU Ziyang^1,2,3, WANG Huihui⁴, LIU Qinwen⁴, YANG Yuling⁴, XU Gengran^1,2,3

1. Surveying and Mapping Institute Lands and Resource Department of Guangdong Province, Guangzhou 510663, China;
2. Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of natural resources, Guangzhou 510663, China;
3. Guangdong Science and Technology Collaborative Innovation Center for Natural Resources, Guangzhou 510663, China;
4. Wuhan Handleray Technology Co., Ltd., Wuhan 430299, China

Received:2021-09-06 Published:2022-06-08

Abstract

Abstract: Building change extraction is one of the important research areas of remote sensing image information extraction, which is of great significance for land survey, natural resources monitoring and land law enforcement. The complex construction structure in Lingnan area of China contains a variety of building structure details, which reflect rich information on the high-resolution remote sensing images, and the factors of influence such as abundant data sources and imaging seasonal differences. It makes the automatic extraction of building change information very difficult. To address this problem, this paper proposes a semantic segmentation model based on HRNet, which achieves the retention of more detailed texture information by screening the feature layers that retain high resolution. On this basis, the automatic detection capability of building changes in high-resolution remote sensing images is improved by GraphCut binarization optimization.

Key words: high-resolution remote sensing images, building change extraction, HRNet, GraphCut

CLC Number:

P237

CHEN Zhilang, FU Zhenhua, ZHU Ziyang, WANG Huihui, LIU Qinwen, YANG Yuling, XU Gengran. HRNet-based extraction of building change information from high-resolution remote sensing images[J]. Bulletin of Surveying and Mapping, 2022, 0(5): 126-132.

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HRNet-based extraction of building change information from high-resolution remote sensing images

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