Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Yongxu Li Xudong Lai Mingwei Wang

Yongxu Li, Xudong Lai, Mingwei Wang. Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1980-2
Citation: Yongxu Li, Xudong Lai, Mingwei Wang. Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1980-2

doi: 10.1007/s13131-021-1980-2

Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Funds: The National Natural Science Foundation of China under contract No. 61971455.
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  • Figure  1.  The framework of the proposed method.

    Figure  2.  Synthetic aperture radar images in three regions of the East China Sea.

    Figure  3.  Labeled samples used as the original labeled samples.

    Figure  4.  Three subimages and the detection results by the constant false alarm rate detector. The green boxes denote the detection results; the red boxes denote the ground truth.

    Figure  5.  Three subimages and the detection results by the Cascade R-CNN. The green boxes denote the detection results; the red boxes denote the ground truth.

    Figure  6.  Ship target discrimination results of subimage No. 3. a. Results by TT. b. Results by D-TT. c. Results by PS-TT. d. Results by the proposed method (correct detections are marked with red boxes, false alarms are marked with green boxes and are numbered, the missed ships are marked with blue boxes and are numbered).

    Figure  7.  Ship target discrimination results of subimage No. 4. a. Results by TT. b. Results by D-TT. c. Results by PS-TT. d. Results by the proposed method (correct detections are marked with red boxes, false alarms are marked with green boxes and are numbered, the missed ships are marked with blue boxes and are numbered).

    Figure  8.  Ship target discrimination results of subimage No. 5. a. Results by TT. b. Results by D-TT. c. Results by PS-TT. d. Results by the proposed method (correct detections are marked with red boxes, false alarms are marked with green boxes and are numbered, the missed ships are marked with blue boxes and are numbered).

    Table  1.   Adopted synthetic aperture radar image features for discrimination

    Feature nameExplanation
    Old-Lincoln featuresstandard deviationThe standard deviation of all the pixels in a target-sized box.$ {F}_{1} $
    fractal dimensionThe Hausdorff dimension of the spatial distribution of strong scatterers in the region of the target-sized box.$ {F}_{2} $
    weighted-rank fill ratioThe power of strong scatterers and normalizing by the total power of all pixels within the target-size box.$ {F}_{3} $
    massThe number of pixels in the target-shaped blob.$ {F}_{4} $
    diameterThe length of the diagonal of the smallest rectangle that encloses the target-shaped blob.$ {F}_{5} $
    square-normalized rotational inertiaThe second mechanical moment of the target-shaped blob around its center, normalized by the inertia of an equal mass square.$ {F}_{6} $
    maximum CFAR statisticThe maximum value in the CFAR image is contained within the target-shaped blob.$ {F}_{7} $
    mean CFAR statisticThe average value of the CFAR image is taken over the target-shaped blob.$ {F}_{8} $
    percent bright CFAR statisticThe percentage of pixels within the target-shaped blob that exceeds a certain CFAR value. The CFAR value is set as AvCS in this paper.$ {F}_{9} $
    specific-entropyThe number of pixels that exceed the threshold that is set to quantity corresponding to the 98th percentile of the surrounding clutter and normalize this value by the total number of pixels in a target-sized box.$ {F}_{10} $
    contiguousnessSegment each image (target-size box and CFAR image) into three separate images (shadow, background, and target) based on the amplitude of individual pixels, then computing numbers from each of these six regions of interest.$ {F}_{11}{-}{F}_{16} $
    New-Lincoln featuresthresholdThe optimal threshold for an image chip is just greater than the clutter background pixel value and smaller than the target pixel value (active pixel).$ {F}_{17} $
    activationThe fraction of pixels that are activated in the optimally thresholded image.$ {F}_{18} $
    dispersionThe weighted average distance from the centroid of a high-intensity pixel on the object, where the weights are assigned in proportion to the mass at each pixel location.$ {F}_{19} $
    inflectionThe rate of change of the mass dispersion statistic at the optimal threshold.$ {F}_{20} $
    accelerationIt measures the acceleration associated with the rate of change of the mass dispersion statistic at the optimal threshold.$ {F}_{21} $
    Gao featuresaverage signal-to-noise-ratioThe average contrast of the target or the false alarms to the background in a candidate chip.$ {F}_{22} $
    peak signal-to-noise-ratioThe peak contrast of the target or the false alarms to the background in a candidate chip.$ {F}_{23} $
    percentage of bright pixelsThe percentage of the brightest pixels with contrast higher than p% of PSNR in all the “active” pixels and p is set to 50 according to the reference.$ {F}_{24} $
    Bhanu featuresprojectionProject the potential target pixels on a horizontal line (or a vertical line, the major diagonal line, the minor diagonal line) and compute the maximum distance.$ {F}_{25}{-}{F}_{28} $
    distanceThe minimum (or maximum, average) distance from each potential target pixel to the centroid.$ {F}_{29}{-}{F}_{31} $
    momentThe horizontal (or vertical, diagonal) second-order distance from each potential target pixel to the centroid.$ {F}_{32}{-}{F}_{34} $
    下载: 导出CSV

    Table  2.   Details of three synthetic aperture radar images

    RegionsAcquire
    time
    Incidence
    angle/(°)
    LatitudeLongitude
    12016-10-3030.72–45.9829.91°–31.82°N120.36°–123.29°E
    22020-03-0130.79–46.0828.07°–29.98°N120.77°–123.66°E
    32020-03-2430.86–46.1726.20°–28.12°N119.13°–121.98°E
    下载: 导出CSV

    Table  3.   Details of the subimages for experimental

    No.RegionsShip targetsFalse alarmsPurpose
    1Region 168369OLSs
    2Region 3162130OLSs
    3Region 127Test
    4Region 298Test
    5Region 270Test
    下载: 导出CSV

    Table  4.   Performance of different models pairing with different feature groups

    No.Feature groupsGaussian SVM/%Linear DA/%Quadratic LR/%Weighted KNN/%Complex DT/%Average/%
    11_VV96.8097.5096.8096.8097.5097.08
    21_VH97.7097.4076.4097.2096.2092.98
    31_VV & VH95.5096.0094.9095.8096.2095.68
    42_VV76.6078.4082.8077.4081.0079.24
    52_VH77.2078.1079.4078.0078.8078.30
    62_VV & VH80.8078.3081.9077.9078.9079.56
    73_VV88.4087.3095.6096.1095.6092.60
    83_VH93.7092.6094.1095.9092.4093.74
    93_VV & VH90.2092.3092.6090.4091.9091.48
    104_VV97.2097.2096.4097.1097.7097.12
    114_VH97.2096.8096.1096.1097.2096.68
    124_VV & VH95.7096.0090.0096.2097.9095.16
    Average90.5890.6689.7591.2491.78
    Note: 1_VV means the first feature group was obtained under VV polarization, and 1_VV & VH means the assembly of the first feature group was obtained under both VV and VH polarization. − represents no data. SVM, support vector machine; DA, discriminant analysis; LR, logistic regression; KNN, knearest neighbor; DT, decision tree.
    下载: 导出CSV

    Table  5.   The performance of five deep learning detectors on the testing set of LS-SSDD-V1.0

    ModelBackboneEpochmAP/%
    Cascade R-CNNResNet50-vd-SSLDv2-FPN1283.25
    Faster R-CNNResNet50-vd-SSLDv2-FPN1281.57
    DCNResNet50-vd-FPN1282.58
    DETRResNet5030058.87
    PP-YOLO v2ResNet50_vd30049.70
    Note: In different backbone, vd means employing ResNet with version D (He et al., 2019), SSLD means employing Simple Semi-supervised Label Distillation (Cui et al., 2021), and FPN means employing Feature Pyramid Networks (Lin et al., 2017).
    下载: 导出CSV

    Table  6.   The results of the CFAR detector and Cascade R-CNN over three subimages

    MethodsMissed
    ships
    False
    alarms
    PoD/%FAR/%FOM/%
    K-CFAR_VV418397.9593.8550.53
    K-CFAR_VH125799.49131.7942.92
    Cascade R-CNN_VV211498.9758.4662.46
    Cascade R-CNN_VH56497.4432.8273.36
    下载: 导出CSV

    Table  7.   The discrimination results of the initial discriminators and refined discriminators

    DiscriminatorsMissed
    ships
    False
    alarms
    PoD/%FAR/%FOM/%
    Initialcomplex Tree11594.36 2.5692.00
    linear DA101294.87 6.1589.37
    weighted KNN113194.3615.9081.42
    Gaussian SVM19790.26 3.5987.13
    quadratic LR201089.74 5.1385.37
    Refinedcomplex Tree9597.44 4.6293.14
    linear DA12398.46 6.1592.75
    weighted KNN8497.95 4.1094.09
    Gaussian SVM4696.92 2.0594.97
    quadratic LR6398.46 3.0895.52
    Note: DA means discriminant analysis.
    下载: 导出CSV

    Table  8.   The discrimination results of the different semisupervised methods

    MethodsMissed shipsFalse alarmsPoD/%FAR/%FOM/%
    TT101094.875.1390.24
    D-TT10594.872.5692.50
    PS-TT5897.444.1093.63
    Proposed method2398.971.5497.47
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-09
  • 录用日期:  2021-12-27
  • 网络出版日期:  2022-02-28

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