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Rural Building Extraction Based on Joint U-Net and the Generalized Chinese Restaurant Franchise from Remote Sensing Images

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  • Zixiong Wang

    (School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China
    Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang 050024, China)

  • Shaodan Li

    (School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China
    Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang 050024, China)

  • Zimeng Zhu

    (School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China
    Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang 050024, China)

Abstract

The extraction of rural buildings from remote sensing images plays a critical role in the development of rural areas. However, automatic building extraction has a challenge because of the diverse types of buildings and complex backgrounds. In this paper, we proposed a two-layer clustering framework named gCRF_U-Net for the extraction of rural buildings. Before the building extraction, the potential built-up areas are firstly detected, which are taken as a constraint for building extraction. Then, the U-Net network is employed to obtain the prior probability of the potential buildings. After this, the calculated probability and the satellite image are put into the generalized Chinese restaurant franchise (gCRF) model to cluster for buildings and non-buildings. In addition, it is worth noting that the hierarchical spatial relationship in the images is clarified for the building extraction. According to the compared experiments on the satellite images and public building datasets, the results show that the proposed method has a better performance, compared with other methods based on the same unified hierarchical models, in terms of quantitative and qualitative evaluation.

Suggested Citation

  • Zixiong Wang & Shaodan Li & Zimeng Zhu, 2023. "Rural Building Extraction Based on Joint U-Net and the Generalized Chinese Restaurant Franchise from Remote Sensing Images," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4685-:d:1089289
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    References listed on IDEAS

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    1. Shaodan Li & Shiyu Fu & Dongbo Zheng, 2022. "Rural Built-Up Area Extraction from Remote Sensing Images Using Spectral Residual Methods with Embedded Deep Neural Network," Sustainability, MDPI, vol. 14(3), pages 1-16, January.
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