Laser & Optoelectronics Progress, Volume. 60, Issue 4, 0401002(2023)

Building Extraction from Remote Sensing Images Based on Improved U-Net

Shu Jin1, Mo Guan1、*, Yuchan Bian2, and Shulei Wang1
Author Affiliations
  • 1School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, Liaoning, China
  • 2School of Software, Shenyang University of Technology, Shenyang 110870, Liaoning, China
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    Figures & Tables(7)
    MA-Unet structure
    Spatial attention module
    CBAM structure
    Contrast effects on the Massachusetts dataset. (a) Original images; (b) label images; (c) SegNet; (d) U-Net; (e) MA-Unet
    Contrast effects on the WHU dataset. (a) Original images; (b) label images; (c) SegNet; (d) U-Net; (e) MA-Unet
    • Table 1. Comparison results on Massachusetts dataset

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      Table 1. Comparison results on Massachusetts dataset

      ModelAccuracy/%Precision/%IoU/%
      U-Net89.8065.2867.61
      PSPnet89.4063.9166.36
      SegNet87.7661.8465.24
      MA-Unet91.5067.3569.24
    • Table 2. Comparison results on WHU dataset

      View table

      Table 2. Comparison results on WHU dataset

      ModelAccuracy/%Precision/%IoU/%
      U-Net96.8688.9786.34
      PSPnet96.6388.3286.56
      SegNet97.1286.2785.74
      MA-Unet97.9290.4188.65
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    Shu Jin, Mo Guan, Yuchan Bian, Shulei Wang. Building Extraction from Remote Sensing Images Based on Improved U-Net[J]. Laser & Optoelectronics Progress, 2023, 60(4): 0401002

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Nov. 19, 2021

    Accepted: Dec. 21, 2021

    Published Online: Feb. 14, 2023

    The Author Email: Guan Mo (42533040@qq.com)

    DOI:10.3788/LOP213004

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