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Acta Optica Sinica, Volume. 41, Issue 23, 2311001(2021)

Small Object Detection in Hyperspectral Images Based on Radial Basis Activation Function

Bofan Wang and Haitao Zhao*
Author Affiliations
  • School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
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    References (31)
    Cited By (5)
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    Figures & Tables(11)
    Schematic diagram of the radial basis activation function for spectral screening (RBAF-SS)

    Fig. 1. Schematic diagram of the radial basis activation function for spectral screening (RBAF-SS)

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    Attention-based resolution reconstruction network (ABRRN)

    Fig. 2. Attention-based resolution reconstruction network (ABRRN)

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    Radial basis object output network (RBOON)

    Fig. 3. Radial basis object output network (RBOON)

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    Examples of hyperspectral data sets. (a) Small objects; (b) medium objects

    Fig. 4. Examples of hyperspectral data sets. (a) Small objects; (b) medium objects

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    Qualitative analysis of spectral selection based on RBAF

    Fig. 5. Qualitative analysis of spectral selection based on RBAF

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    Spectral curve of airplane object features

    Fig. 6. Spectral curve of airplane object features

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    Experimental results of the proposed method are compared with four approaches with sigmoid based object output layers. (a) Faster RCNN[1] (ResNet-50); (b) YOLOv3[25] (Darknet-53); (c) FCOS[32] (ResNet-50); (d) CenterNet[24] (ResNet-18); (e) proposed method (ResNet-18)

    Fig. 7. Experimental results of the proposed method are compared with four approaches with sigmoid based object output layers. (a) Faster RCNN[1] (ResNet-50); (b) YOLOv3[25] (Darknet-53); (c) FCOS[32] (ResNet-50); (d) CenterNet[24] (ResNet-18); (e) proposed method (ResNet-18)

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    False alarm rate of different approaches under different IoU threshold

    Fig. 8. False alarm rate of different approaches under different IoU threshold

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    • Table 1. Overall structure of the detection network

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      Table 1. Overall structure of the detection network

      StageSub-networkBlockLayer detailsOutput shape /(pixel×pixel×pixel)
      Input----384×192×25
      1ABRRN-DeconvConv+ReLUConvRBAF-SSGAP768×384×6
      ABRRN-Element-wise multiplicationSame as the above ABRRN1536×768×3
      2ResNet-18backboneDown samplingConv+BN+ReLU384×192×64
      Max pooling
      ResBlock(no down sampling)Conv+BN+ReLUConv+BNIdentity384×192×64
      Element-wise Addition
      ResBlock(down sampling)Conv+BN+ReLUConv+BNConv+BN192×96×128
      Element-wise addition
      ResBlock(no down sampling)Same as the above ResBlock(no down sampling)192×96×128
      ResBlock(down sampling)Same as the above ResBlock(down sampling)96×48×256
      ResBlock(no down sampling)Same as the above ResBlock(no down sampling)96×48×256
      ResBlock(down sampling)Same as the above ResBlock(down sampling)48×24×512
      Up sampling blockDCN+BN+ReLUDeconv+BN+ReLU96×48×256
      3Up samplingnetworkUp sampling blockSame as the above upsampling block96×48×128
      Up sampling blockSame as the above upsampling block96×48×64
      4RBOON-Conv+ReLUConv+ReLUConv+ReLU
      RBAF-SODConvConv96×48×1/2/2

    • Table 2. Detection accuracy and ablation experiment

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      Table 2. Detection accuracy and ablation experiment

      MethodsAP50 /%AP50:95 /%Time /s
      SmallMediumAllSmallMediumAll
      Faster RCNN[1] (ResNet-50)59.469.562.221.830.424.20.044
      YOLOv3[25](Darknet-53)51.867.456.218.528.521.30.015
      FCOS[32](ResNet-50)25.046.731.19.09.78.60.041

    • Table 3. Ablation experiment

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      Table 3. Ablation experiment

      MethodsAP50 /%AP50:95 /%Time /s
      SmallMediumAllSmallMediumAll
      CenterNet[24](ResNet-18)60.270.463.121.528.322.70.011
      Proposed-ABRRN(ResNet-18)64.973.867.423.427.024.40.020
      Ours-RBOON(ResNet-18)60.770.663.521.628.422.90.011
      Proposed(ResNet-18)65.074.267.623.526.724.40.020
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    Bofan Wang, Haitao Zhao. Small Object Detection in Hyperspectral Images Based on Radial Basis Activation Function[J]. Acta Optica Sinica, 2021, 41(23): 2311001

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    Paper Information

    Category: Imaging Systems

    Received: Apr. 15, 2021

    Accepted: Jun. 10, 2021

    Published Online: Nov. 29, 2021

    The Author Email: Zhao Haitao (Haitaozhao@ecust.edu.cn)

    DOI:10.3788/AOS202141.2311001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology