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    Journals >Laser & Optoelectronics Progress >Volume 61 >Issue 18 >Page 1828001 > Article
    • Laser & Optoelectronics Progress
    • Vol. 61, Issue 18, 1828001 (2024)
    Change Detection of Optical and Synthetic Aperture Radar Remote Sensing Images Based on a Domain Adaptive Neural Network
    Qinfeng Yao1,*, Yongxiang Ning1, and Sunwen Du2
    Author Affiliations
  • 1Department of Earth Science and Engineering, Shanxi Institute of Engineering and Technology, Yangquan 045000, Shanxi, China
  • 2School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
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    DOI: 10.3788/LOP232565 Cite this Article Set citation alerts
    Qinfeng Yao, Yongxiang Ning, Sunwen Du. Change Detection of Optical and Synthetic Aperture Radar Remote Sensing Images Based on a Domain Adaptive Neural Network[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1828001 Copy Citation Text show less
    Overall architecture of proposed method
    Fig. 1. Overall architecture of proposed method
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    Dual attention mechanism of spatial channel. (a) Convolution block attention module; (b) spatial access attention module; (c) spatial attention module; (d) channel attention module
    Fig. 2. Dual attention mechanism of spatial channel. (a) Convolution block attention module; (b) spatial access attention module; (c) spatial attention module; (d) channel attention module
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    Dataset 1. (a) Landsat-8 optical image; (b) Sentinel-1A SAR image; (c) ground truth
    Fig. 3. Dataset 1. (a) Landsat-8 optical image; (b) Sentinel-1A SAR image; (c) ground truth
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    Dataset 2. (a) QuickBird-2 optical image; (b) TerraSAR-X StripMap HH SAR image; (c) ground truth
    Fig. 4. Dataset 2. (a) QuickBird-2 optical image; (b) TerraSAR-X StripMap HH SAR image; (c) ground truth
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    Dataset 3. (a) Sentinel-2 optical image; (b) COSMO-SkyMed SAR image; (c) ground truth
    Fig. 5. Dataset 3. (a) Sentinel-2 optical image; (b) COSMO-SkyMed SAR image; (c) ground truth
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    Change detection results of dataset 1. (a) Optical image; (b) SAR image;(c) DCCN; (d) DHFF; (e) EECD; (f) FCSN; (g) DTCDN; (h) MSCD; (i) proposed method; (j) ground truth
    Fig. 6. Change detection results of dataset 1. (a) Optical image; (b) SAR image;(c) DCCN; (d) DHFF; (e) EECD; (f) FCSN; (g) DTCDN; (h) MSCD; (i) proposed method; (j) ground truth
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    Change detection results of dataset 2. (a) Optical image; (b)SAR image; (c) DCCN; (d) DHFF; (e) EECD; (f) FCSN; (g) DTCDN; (h) MSCD; (i) proposed method; (j) ground truth
    Fig. 7. Change detection results of dataset 2. (a) Optical image; (b)SAR image; (c) DCCN; (d) DHFF; (e) EECD; (f) FCSN; (g) DTCDN; (h) MSCD; (i) proposed method; (j) ground truth
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    Change detection results of dataset 3. (a) Optical images; (b) SAR image;(c) DCCN; (d) DHFF; (e) EECD; (f) FCSN; (g) DTCDN; (h) MSCD; (i) proposed method; (j) ground truth
    Fig. 8. Change detection results of dataset 3. (a) Optical images; (b) SAR image;(c) DCCN; (d) DHFF; (e) EECD; (f) FCSN; (g) DTCDN; (h) MSCD; (i) proposed method; (j) ground truth
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    MethodRprecisionRrecallRmIOUsF1
    DCCN49.9249.6938.8446.31
    DHFF55.7573.4544.4154.50
    EECD73.1681.9266.8976.68
    FCSN77.2180.0268.6478.26
    DTCDN80.2982.0671.2880.67
    MSCD79.8984.2272.2581.54
    Proposed method78.8985.3872.7482.17
    Table 1. Quantitative evaluation results for dataset 1
    MethodRprecisionRrecallRmIOUsF1
    DCCN47.1343.9138.9845.02
    DHFF52.9458.1741.3751.18
    EECD89.0891.0182.3589.52
    FCSN91.9994.5487.1792.75
    DTCDN88.8395.1785.3291.57
    MSCD91.6093.3186.2492.17
    Proposed method92.0495.8888.8893.86
    Table 2. Quantitative evaluation results for dataset 2
    MethodRprecisionRrecallRmIOUsF1
    DCCN50.1450.1140.5750.01
    DHFF48.2147.0736.5347.62
    EECD61.656.5948.6557.71
    FCSN61.5958.2448.8557.74
    DTCDN67.4265.3654.9266.29
    MSCD68.6774.2457.4370.01
    Proposed method71.5171.9159.9171.71
    Table 3. Quantitative evaluation results for dataset 3
    MethodTraining timePrediction time
    EECD5.980.065
    FCSN7.120.037
    DTCDN266.150.218
    MSCD35.820.061
    Proposed method26.190.058
    Table 4. Comparison of training time and prediction time
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    Qinfeng Yao, Yongxiang Ning, Sunwen Du. Change Detection of Optical and Synthetic Aperture Radar Remote Sensing Images Based on a Domain Adaptive Neural Network[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1828001
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    Paper Information
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