Automatic Building Extraction from Remote Sensing Images Based on Cascaded CRFs and the U-Net Deep Learning Model

CHEN Jiahao; XING Hanfa; CHEN Xianglong

doi:10.6054/j.jscnun.2022011

Journal of South China Normal University (Natural Science Edition) > 2022 > 54(1): 70-78. > DOI: 10.6054/j.jscnun.2022011

CHEN Jiahao, XING Hanfa, CHEN Xianglong. Automatic Building Extraction from Remote Sensing Images Based on Cascaded CRFs and the U-Net Deep Learning Model[J]. Journal of South China Normal University (Natural Science Edition), 2022, 54(1): 70-78. DOI: 10.6054/j.jscnun.2022011

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Automatic Building Extraction from Remote Sensing Images Based on Cascaded CRFs and the U-Net Deep Learning Model

1.
Guangdong Shengteng Geospatial Information Technology, Foshan 528200, China
2.
School of Geography, South China Normal University, Guangzhou 510631, China
3.
SCNU Qingyuan Institute of Science and Technology Innovation Co., Ltd., Qingyuan 511517, China
4.
Guangdong Normal University Weizhi Information Technology Co., Ltd., Qingyuan 511517, China

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Received Date: March 16, 2021
Available Online: March 13, 2022

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Abstract

Abstract

To address the problem of building edge ambiguity in deep learning models for building extraction, cascaded CRFs (fully connected conditional random fields) is introduced into the U-Net model and an improved U-Net model (U-Net+cascade CRFs) is proposed for automatic building extraction from remote sensing images. A cascaded CRFs model is constructed and introduced into the decoding layer of the U-Net model to learn the boundary information from the multi-layer structure and enhance the ability of the model to segment the building boundary. Taking Foshan City, Guangdong Province, as the research area, the U-Net+cascaded CRFs, U-Net+CRFs, U-Net and SVM models are used to carry out building extraction experiments. The results show that the proposed method can effectively identify building boundary information and improve the accuracy of building extraction: it can achieve 93.1%, 87.5%, 91.4% and 85.1% of the four indexes of accuracy, recall rate, F1 value and cross/combine ratio respectively, which are superior to those of U-Net+CRFs, U-Net and SVM models.
- deep learning,
- building extraction,
- U-Net,
- full connected condition random fields

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References (25)

References

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