Mangrove monitoring and extraction based on multi-source remote sensing data: a deep learning method based on SAR and optical image fusion

Yiheng Xie Xiaoping Rui Yarong Zou Heng Tang Ninglei Ouyang

Yiheng Xie, Xiaoping Rui, Yarong Zou, Heng Tang, Ninglei Ouyang. Mangrove monitoring and extraction based on multi-source remote sensing data: a deep learning method based on SAR and optical image fusion[J]. Acta Oceanologica Sinica, 2024, 43(9): 110-121. doi: 10.1007/s13131-024-2356-1
Citation: Yiheng Xie, Xiaoping Rui, Yarong Zou, Heng Tang, Ninglei Ouyang. Mangrove monitoring and extraction based on multi-source remote sensing data: a deep learning method based on SAR and optical image fusion[J]. Acta Oceanologica Sinica, 2024, 43(9): 110-121. doi: 10.1007/s13131-024-2356-1

doi: 10.1007/s13131-024-2356-1

Mangrove monitoring and extraction based on multi-source remote sensing data: a deep learning method based on SAR and optical image fusion

Funds: The Key R&D Project of Hainan Province under contract No. ZDYF2023SHFZ097; the National Natural Science Foundation of China under contract No. 42376180.
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  • Figure  1.  Geographical location, SAR image, and optical image of the study area.

    Figure  2.  Weighted fusion result of SAR image and optical image.

    Figure  3.  Diagram of the AttU-Net model structure. Conv, convergence.

    Figure  4.  Diagram of network structure of SE-Net.

    Figure  5.  Experimental comparison diagram of a comparison experiment. The white area in the image represents the area considered by the model as mangrove vegetation. The black area represents the area where the model considers non-mangrove vegetation. The red dashed box indicates the area with obvious recognition error.

    Figure  6.  Comparison of predicted results of ablation experiments. The white area in the image represents the area considered by the model as mangrove vegetation. The black area represents the area where the model considers non-mangrove vegetation. The red dashed box indicates the area with obvious recognition error.

    Figure  7.  Line charts of accuracy and loss values of the training set and test set based on fusion images.

    Figure  8.  Comparison of prediction results of mangrove vegetation in the test area. The white area in the image represents the area considered by the model as mangrove vegetation. The black area represents the area where the model considers non-mangrove vegetation. The red dashed box indicates the area with obvious recognition error.

    Table  1.   Full polarization imaging mode and capability of Gaofen-3 SAR image

    Serial
    number
    Working modeAngle of
    incidence/(°)
    Visual number
    A × E
    Resolution/mImaging bandwidth/kmPolarization
    mode
    Wave
    position
    NominalAzimuth
    direction
    Distance
    direction
    NominalScope
    1fully polarized band 120–411 × 1886−93020–35full polarizationQ1–Q28
    2fully polarized band 220–383 × 2252515–304035–50full polarizationWQ1–WQ16
    3wave pattern20–411 × 210108–125 × 55 × 5full polarizationQ1–Q28
    下载: 导出CSV

    Table  2.   Satellite payload of Gaofen-6

    Camera type Band number Spectrum/μm Substellar point pixel resolution Covering width
    Off-axis TMA total reflection type panchromatic band (P) 0.45–0.90 full color: better than 2 m >90 km
    Off-axis TMA total reflection type blue spectrum (B1) 0.45–0.52 multispectral: better than 8 m >90 km
    Off-axis TMA total reflection type green spectrum (B2) 0.52–0.60 multispectral: better than 8 m >90 km
    Off-axis TMA total reflection type red band (B3) 0.63–0.69 multispectral: better than 8 m >90 km
    Off-axis TMA total reflection type near-infrared spectrum (B4) 0.76–0.90 multispectral: better than 8 m >90 km
    下载: 导出CSV

    Table  3.   Layout of confusion matrix

    Prediction typeReal type
    TerraceNon-terraced field
    TerraceTP (true positive)FP (false positive)
    Non-terraced fieldFN (false negative)TN (true negative)
    下载: 导出CSV

    Table  4.   Precision evaluation results of comparison experiments based on fusion images

    Contrast region (a:b) F1-score/% OA/% Kappa/%
    0:10 96.696 94.347 77.192
    1:9 98.282 97.016 86.968
    2:8 98.567 97.506 88.959
    3:7 96.688 94.350 77.542
    4:6 96.067 93.349 74.674
    5:5 97.643 95.936 82.919
    6:4 96.039 93.257 73.462
    7:3 98.067 96.598 83.897
    8:2 96.969 94.721 76.504
    9:1 96.438 93.835 73.547
    10:0 95.635 92.481 68.569
    下载: 导出CSV

    Table  5.   Accuracy evaluation results of ablation test area

    No.BaseSE-NetDrop.BNOA/%F1-score/%Kappa/%
    195.03897.10779.694
    296.94898.23986.797
    396.69798.09285.814
    493.80496.35275.891
    595.75597.52982.494
    694.96297.07379.015
    795.20397.20680.302
    897.50798.56888.959
    Note: √ in the table proves that the module is added to the model; if √ is not marked, it proves that the module is not added to the model. Bold font denotes the highest value in this accuracy evaluation metric.
    下载: 导出CSV

    Table  6.   Training parameter settings

    Parameter Specific setting
    Batch size 16
    Learning rate 1 × 10–4
    Epoch 65
    Optimizer Adam
    下载: 导出CSV

    Table  7.   Accuracy evaluation results of test area

    Test area Model Accuracy evaluation
    OA/% F1-Score/% Kappa/%
    Test area 1 AttU-Net (ours) 97.082 88.008 86.348
    U-Net 95.870 81.583 79.268
    Seg-Net 71.099 39.136 25.900
    Dense-Net 95.056 75.064 72.445
    Res-Net 94.974 75.102 72.410
    Test area 2 AttU-Net (ours) 97.506 98.567 88.959
    U-Net 95.038 97.107 79.694
    Seg-Net 92.363 95.753 58.229
    Dense-Net 94.571 96.835 80.925
    Res-Net 94.083 96.524 76.728
    Test area 3 AttU-Net (ours) 93.952 87.878 83.851
    U-Net 93.553 86.171 82.009
    Seg-Net 51.064 50.002 19.944
    Dense-Net 91.625 82.041 76.633
    Res-Net 92.383 83.158 78.328
    Test area 4 AttU-Net (ours) 89.083 85.572 77.021
    U-Net 85.762 80.093 69.644
    Seg-Net 83.485 82.329 67.046
    Dense-Net 78.289 65.889 52.542
    Res-Net 80.267 69.966 57.147
    Note: Bold font denotes the highest value in this accuracyevalu-ation metric.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-01-09
  • 录用日期:  2024-06-24
  • 网络出版日期:  2024-08-01
  • 刊出日期:  2024-09-01

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