Laser & Optoelectronics Progress, Volume. 61, Issue 8, 0828003(2024)

Lightweight Bilateral Input D-WNet Aerial Image Building Change Detection

Fengxing Zhang1, Jian Huang2, and Hao Li1、*
Author Affiliations
  • 1College of Earth Science and Engineering, Hohai University, Nanjing 211000, Jiangsu, China
  • 2Jiangsu Academy of Surveying and Mapping Engineering, Nanjing 211000, Jiangsu, China
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    Figures & Tables(14)
    Two-side input W-Net
    Implementation process of CBAM
    Channel attention module
    Spatial attention module
    Lightweight bilateral input D-WNet
    Two-period images and corresponding labels in the datasets
    Image processing results of line feature extraction module
    Comparison of ablation results of shape feature selection experiment
    Comparison of ablation results of network structure improvement experiment
    Feature extraction heat map of the network structure improved ablation model
    Building change detection results for different networks on WHU and LEVIR-CD test sets
    • Table 1. Comparison of experimental evaluation indexes of shape feature selection

      View table

      Table 1. Comparison of experimental evaluation indexes of shape feature selection

      DatasetNetworkInput_leftInput_rightRrecallRprecisionsF1RIoU
      WHUAFormer phase imagePost-temporal image98.0197.6197.8195.54
      BBiphasic imageCanny96.7198.3097.5095.07
      CBiphasic imageSobel97.4298.1797.8095.56
      D-WNetBiphasic imageCanny+Sobel97.4998.8198.1496.36
      LEVIR-CDAFormer phase imagePost-temporal image90.8291.4091.1183.36
      BBiphasic imageCanny89.7691.8490.7982.94
      CBiphasic imageSobel89.4091.9890.6783.25
      D-WNetBiphasic imageCanny+Sobel90.2392.2591.2383.49
    • Table 2. Comparison of experimental evaluation indexes of network structure improvement

      View table

      Table 2. Comparison of experimental evaluation indexes of network structure improvement

      DatasetNetworkCBAM

      Lightweight

      structure

      ASPPRrecall /%Rprecision /%sF1 /%RIoU /%Epoch time /s
      WHUD××97.9697.9098.0396.021502
      E×96.6098.5597.5695.25438
      F×97.6997.6097.6495.97560
      D-WNet97.4998.8198.1496.36454
      LEVIR-CDD××90.2992.1391.2083.38786
      E×88.2192.2090.1682.67235
      F×91.2290.7190.9683.22312
      D-WNet90.2392.2591.2383.49260
    • Table 3. Building change detection accuracy of different networks on WHU and LEVIR-CD test set

      View table

      Table 3. Building change detection accuracy of different networks on WHU and LEVIR-CD test set

      DatasetNetworkRrecall /%Rprecision /%sF1 /%RIoU /%Epoch time /s
      WHUU-Net97.8298.2598.0396.16555
      SENet97.9598.1398.0496.31581
      ResNet97.7598.0497.8995.88455
      DeepLabv3+96.7797.5697.1694.49224
      D-WNet97.4998.8198.1496.36454
      LEVIR-CDU-Net88.5392.1790.3182.55365
      SENet89.0492.2190.6082.56374
      ResNet88.9191.7390.3082.14262
      DeepLabv3+88.0692.0890.0381.86160
      D-WNet90.2392.2591.2383.49260
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    Fengxing Zhang, Jian Huang, Hao Li. Lightweight Bilateral Input D-WNet Aerial Image Building Change Detection[J]. Laser & Optoelectronics Progress, 2024, 61(8): 0828003

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Jun. 8, 2023

    Accepted: Jul. 24, 2023

    Published Online: Mar. 15, 2024

    The Author Email: Li Hao (lihao@hhu.edu.cn)

    DOI:10.3788/LOP231478

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