Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images

Kai Du Yi Ma Zongchen Jiang Xiaoqing Lu Junfang Yang

Kai Du, Yi Ma, Zongchen Jiang, Xiaoqing Lu, Junfang Yang. Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1977-x
Citation: Kai Du, Yi Ma, Zongchen Jiang, Xiaoqing Lu, Junfang Yang. Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1977-x

doi: 10.1007/s13131-021-1977-x

Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images

Funds: The National Natural Science Foundation of China under contract No. 61890964.
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    Corresponding author: mayimail@fio.org.cn
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  • Figure  1.  Study area 1. a. Geographical location of study area 1. b. The true color RGB image of area 1. c. The ${\mathit{\theta }}_{\rm{m}}$ of study area 1. d. The spectra of oil emulsions, oil slicks, and seawater.

    Figure  2.  Study area 2 and study area 3. a. Geographical location of study area 1 and study area 2. b. The true color RGB image of area 2. c. The true color RGB image of area 3. d. The ${\theta }_{{\rm{m}}}$ of study area 2. e. The ${\theta }_{{\rm{m}}}$ of study area 3.

    Figure  3.  Spectra of study area 2 (a) and study area 3 (b).

    Figure  4.  Process of data augmentation.

    Figure  5.  Convolution neural networks structure.

    Figure  6.  Classification accuracy of oil spill under different sizes of spatial neighborhoods.

    Figure  7.  The time cost of different spatial neighborhood sizes.

    Figure  8.  Influence of data augmentation on the classification accuracy of oil emulsions and oil slicks. a. F1-Score of oil emulsions. b. F1-Score of oil slicks.

    Figure  9.  Influence of the values of $ \mathit{\alpha } $ and $ \mathit{\beta } $ on oil spill classification accuracy. a. The F1-Score of oil emulsions. b. The F1-Score of oil slicks.  

    Figure  10.  Comparison of the results of the different loss functions.

    Figure  11.  F1-Score of oil emulsions and oil slicks comparisons in different methods.

    Figure  12.  Classification results of different methods. a. Result of visual interpretation. b. Result of the CBF-CNN model. c. Result of the DNN model. d. Result of the SVM. e. Result of the RF model.

    Figure  13.  Spatial distribution of training data (a) and test data (b).

    Figure  14.  F1-Score of oil emulsions and oil slicks comparisons in different methods.

    Figure  15.  The results of different classification methods. a. Classification result of the CBF-CNN. b. Classification result of the DNN. d. Classification result of the SVM. e. Classification result of the RF.

    Figure  16.  The logarithmic transformation of the image. a. Origin image; b. transformed image.

    Figure  17.  Training (a) and test data (b) of study area 3.

    Figure  18.  The results of different classification methods in study area 3. a. Classification result of the CBF-CNN. b. Classification result of the DNN. d. Classification result of the SVM. e. Classification result of the RF.

    Figure  19.  Various oil spills could be identified from HY-1C CZI RGB images.

    Table  1.   Main technical specifications of the HY-1C CZI

    BandsWavelength/nmSpatial resolution/mSignal-noise radio/dB
    1420–50050410
    2520–60050300
    3610–69050248
    4760–89050240
    下载: 导出CSV

    Table  2.   Parameters of different models

    ModelsParameters
    CBF-CNNEpoch: 100; Batch size: 450; Optimizer: Adam; Loss fun
    ction: CBF; Learning rate: 0.001; Spatial neighborhood-scale: 11×11
    CNNEpoch: 100; Batch size: 450; Optimizer: Adam; Loss fun
    ction: cross entropy; Learning rate: 0.001; Spatial neigh
    borhood-scale: 11×11
    DNNHidden layer sizes: 50; Activation: Relu; Optimizer: Ada
    m; Learning rate: 0.2; Epoch: 200
    SVMKernel: RBF; C: 700; Gamma: 0.25; Degree: 3
    RFThe number of the trees: 90; The minimum number of s
    amples on leaf: 50; The maximum number of elements: 5
    下载: 导出CSV

    Table  3.   Confusion matrices of different methods

    MethodsClassSeawaterOil emulsionsOil slicks
    CBF loss-CNNseawater699 12311 667
    oil emulsion1708 7041 576
    oil slick2 85784954 021
    DNNseawater700 30325463
    oil emulsion5277 9431 980
    oil slick23 06086933 798
    SVMseawater682 3609718 334
    oil emulsion2818 0542 115
    oil slick9 6891 12446 914
    RFseawater688 6201 89810 273
    oil emulsion1988 3871 865
    oil slick10 7261 66445 337
    下载: 导出CSV

    Table  4.   Statistics of training data and test data

    ClassOil emulsionsOil slicksBackgroundTotal
    Train28836912 48513 142
    Test9241 00031 31133 235
    Total1 2121 36943 79646 377
    下载: 导出CSV

    Table  5.   Parameters of different models

    ModelsParameters
    CBF-CNNEpoch: 100; Batch size: 450; Optimizer: Adam; Loss function: CBF; Learning rate: 0.001; Spatial neighborhood-scale: 11×11
    DNNHidden layer sizes: (100, 50); Activation: Relu; Optimizer: Adam; Learning rate: 0.2; Epoch: 200
    SVMKernel: RBF; C: 500; Gamma: 0.5; Degree: 3
    RFThe number of the trees: 100; The minimum number of samples on leaf: 50; The maximum number of elements: 5
    下载: 导出CSV

    Table  6.   Statistics of train data and test data

    ClassOil slicksBackgroundTotal
    Training4 11110 58414 695
    Test7 79017 78225 572
    Total11 90128 36640 267
    下载: 导出CSV

    Table  7.   Parameters of different models

    ModelsParameters
    CBF-CNNEpoch: 100; Batch size: 450; Optimizer: Adam; Loss function: CBF; Learning rate: 0.001; Spatial neighborhood-scale: 11×11
    DNNHidden layer sizes: (100, 70); Activation: Relu; Optimizer: Adam; Learning rate: 0.2; Epoch: 200
    SVMKernel: RBF; C: 800; Gamma: 0.35; Degree: 3
    RFThe number of the trees: 80; The minimum number of samples on leaf: 20; The maximum number of elements: 3
    下载: 导出CSV

    Table  8.   Detection performance of different methods for oil slicks

    MethodsPrecisionRecallF1-Score
    CBF-CNN0.940.970.96
    DNN0.690.840.76
    SVM0.660.860.75
    RF0.700.760.73
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-04
  • 录用日期:  2021-11-12
  • 网络出版日期:  2022-01-25

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