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CA-BASNet: A Building Extraction Network in High Spatial Resolution Remote Sensing Images

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  • Liang Huang

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Center for Geo-Informatics Technology Surveying and Mapping in the Plateau Mountains of Yunnan Higher Education, Kunming 650093, China)

  • Juanjuan Zhu

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Mulan Qiu

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Xiaoxiang Li

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Shasha Zhu

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

Abstract

Aiming at the problems of holes, misclassification, and rough edge segmentation in building extraction results from high spatial remote sensing images, a coordinate attention mechanism fusion network based on the BASNet network (CA-BASNet) is designed for building extraction in high spatial remote sensing images. Firstly, the deeply supervised encoder–decoder network was used to create a rough extract of buildings; secondly, to make the network pay more attention to learning building edge features, the mixed loss function composed of binary cross entropy, structural similarity and intersection-over-union was introduced into the network training process; finally, the residual optimization module of fusion coordinate attention mechanism was used for post-processing to realize the fine extraction of buildings from high spatial resolution remote sensing images. Experiments on the WHU building dataset show that the proposed network can achieve mIoU of 93.43%, mPA of 95.86%, recall of 98.79%, precision of 90.13% and F1 of 91.35%. Compared with the existing semantic segmentation networks, such as PSPNet, SegNet, DeepLapV3, SE-UNet, and UNet++, the accuracy of the proposed network and the integrity of object edge segmentation are significantly improved, which proves the effectiveness of the proposed network.

Suggested Citation

  • Liang Huang & Juanjuan Zhu & Mulan Qiu & Xiaoxiang Li & Shasha Zhu, 2022. "CA-BASNet: A Building Extraction Network in High Spatial Resolution Remote Sensing Images," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11633-:d:916681
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    References listed on IDEAS

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    1. Pieter-Tjerk de Boer & Dirk Kroese & Shie Mannor & Reuven Rubinstein, 2005. "A Tutorial on the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 19-67, February.
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    Cited by:

    1. Daoyuan Zheng & Jianing Kang & Kaishun Wu & Yuting Feng & Han Guo & Xiaoyun Zheng & Shengwen Li & Fang Fang, 2023. "Semi-Supervised Building Detection from High-Resolution Remote Sensing Imagery," Sustainability, MDPI, vol. 15(15), pages 1-22, August.

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