• Acta Optica Sinica
  • Vol. 37, Issue 10, 1015002 (2017)
Feng Liu1、*, Tongsheng Shen2, and Xinxing Ma1
Author Affiliations
  • 1 Department of Control Engineering, Naval Aeronautical and Astronautical University, Yantai, Shandong 264001, China
  • 2 China Defense Science and Technology Information Center, Beijing 100142, China
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    DOI: 10.3788/AOS201737.1015002 Cite this Article Set citation alerts
    Feng Liu, Tongsheng Shen, Xinxing Ma. Convolutional Neural Network Based Multi-Band Ship Target Recognition with Feature Fusion[J]. Acta Optica Sinica, 2017, 37(10): 1015002 Copy Citation Text show less
    Flow of the proposed algorithm
    Fig. 1. Flow of the proposed algorithm
    Diagram of the network structure
    Fig. 2. Diagram of the network structure
    Contrast of parameters for different layers
    Fig. 3. Contrast of parameters for different layers
    Examples in target identification database. (a) Cruise A; (b) cruise B; (c) fisher; (d) railway ferry; (e) warship; (f) merchant ship
    Fig. 4. Examples in target identification database. (a) Cruise A; (b) cruise B; (c) fisher; (d) railway ferry; (e) warship; (f) merchant ship
    Dimension selection of fusion features
    Fig. 5. Dimension selection of fusion features
    Recognition rate matrices of the proposed method. (a) Proposed fusion recognition; (b) visible light; (c) medium-wave infrared; (d) long-wave infrared
    Fig. 6. Recognition rate matrices of the proposed method. (a) Proposed fusion recognition; (b) visible light; (c) medium-wave infrared; (d) long-wave infrared
    Partial images of false recognition
    Fig. 7. Partial images of false recognition
    Parameter LayerStructure of network
    Conv_1Conv_2Conv_3Conv_4Fc_5Fc_6
    StructureC+R+L+PC+R+PC+R+PC+R+PF+R+DF+R+D
    Input227×227×327×27×9613×13×25613×13×3846×6×2564096
    Neuron9625638425640964096
    Kernal size11×115×53×33×31×11×1
    Stride4211
    Pooling3×33×33×3
    Pooling stride222
    Train parameter96×(11×11×3+1)256×(5×5×96+1)384×(3×3×256+1)256×(3×3×384+1)4096×(3×3×256+1)4096×(4096+1)
    Table 1. Structure and parameters of the neural network
    MethodRecognition rate /%
    Visible lightMedium-wave infraredLong-wave infraredFusion recognition
    HOG+SVM63.255.053.5
    SIFT67.360.255.2
    AlexNet75.666.567.7
    VGG-1677.368.369.2
    Proposed75.167.268.184.5
    Table 2. Recognition rate comparison of different methods
    Feng Liu, Tongsheng Shen, Xinxing Ma. Convolutional Neural Network Based Multi-Band Ship Target Recognition with Feature Fusion[J]. Acta Optica Sinica, 2017, 37(10): 1015002
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