• Laser & Optoelectronics Progress
  • Vol. 59, Issue 22, 2228004 (2022)
Xiaoyu Yang and Xili Wang*
Author Affiliations
  • School of Computer Science, Shaanxi Normal University, Xi'an 710119, Shaanxi, China
  • show less
    DOI: 10.3788/LOP202259.2228004 Cite this Article Set citation alerts
    Xiaoyu Yang, Xili Wang. Building Segmentation Model of Remote Sensing Image Combining Multiscale Attention and Edge Supervision[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2228004 Copy Citation Text show less
    Structure of MAE-Net
    Fig. 1. Structure of MAE-Net
    Multiscale attention module
    Fig. 2. Multiscale attention module
    Split convolution concat module
    Fig. 3. Split convolution concat module
    SEWeight module
    Fig. 4. SEWeight module
    Edge extraction module
    Fig. 5. Edge extraction module
    Segmentation results of ablation experiment on WHU Building test set. (a) Original images; (b) image label;(c) baseline processing result; (d) baseline with MSA processing result; (e) baseline with EEB processing result; (f) MAE-Net processing result
    Fig. 6. Segmentation results of ablation experiment on WHU Building test set. (a) Original images; (b) image label;(c) baseline processing result; (d) baseline with MSA processing result; (e) baseline with EEB processing result; (f) MAE-Net processing result
    Segmentation results of comparative experiment on WHU Building validation set.(a) Original images; (b) image label; (c) U-Net processing result; (d) MAE-Net processing result
    Fig. 7. Segmentation results of comparative experiment on WHU Building validation set.(a) Original images; (b) image label; (c) U-Net processing result; (d) MAE-Net processing result
    Segmentation results of comparative experiment on Satellite Dataset Ⅱ (East Asia) test set. (a) Original images; (b) image label; (c) U-Net processing result; (d) MAE-Net processing result
    Fig. 8. Segmentation results of comparative experiment on Satellite Dataset Ⅱ (East Asia) test set. (a) Original images; (b) image label; (c) U-Net processing result; (d) MAE-Net processing result
    No.BaselineMSAEEBF1-scoreIOU
    1××0.94200.8904
    2×0.95020.9052
    3×0.94960.9041
    40.95200.9084
    Table 1. Comparison of evaluation results of ablation experiment on WHU Building test set
    No.BaselineMSAEEBTrain time /hTest time per picture /s
    1××5.130.077
    2×8.160.078
    3×6.830.066
    49.130.089
    Table 2. Comparison of train and test time of ablation experiment on WHU Building test set
    ScaleU-NetMAE-Net
    IOUF1-scoreIOUF1-score
    Small-scale(image 1+image 2)0.9040.9490.9100.953
    Large-scale(image 5+image 6)0.9180.9570.9270.963
    Edge-scale(image 3+image 4)0.4380.6090.5450.706
    Table 3. Accuracy results of two models for building and building edge recognition
    MethodF1-scorePRIOU
    U-Net60.91350.95420.88260.8813
    SiU-Net160.93850.93800.93900.8840
    SRINet170.94230.95210.93280.8909
    Ra-CGAN10.94900.95100.94600.8960
    DeNet180.94800.95000.94600.9012
    MAE-Net0.95420.95540.95300.9124
    Table 4. Experimental results of different methods on WHU building validation set
    MethodF1-scorePRIOU
    U-Net60.7460.6530.8690.594
    SiU-Net160.7580.7250.7960.611
    AugU-Net190.7800.640
    RSIS-MLCA200.7960.8260.7680.661
    Ra-CGAN10.8120.8520.7750.677
    MAE-Net0.8020.8280.8050.690
    Table 5. Experimental results of different methods with the Satellite Dataset Ⅱ (East Asia) test set
    Xiaoyu Yang, Xili Wang. Building Segmentation Model of Remote Sensing Image Combining Multiscale Attention and Edge Supervision[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2228004
    Download Citation