Acta Optica Sinica, Volume. 39, Issue 4, 0410001(2019)

Landform Image Classification Based on Sparse Coding and Convolutional Neural Network

Fang Liu, Xin Wang*, Lixia Lu, Guangwei Huang, and Hongjuan Wang
Author Affiliations
  • Information Department, Beijing University of Technology, Beijing 100022, China
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    Figures & Tables(10)
    Dictionary visualization expression. (a) Example 1; (b) example 2
    Model based on SC and CNN
    Algorithm flowchart of landform scene classification based on SC and CNN
    Feature visualization of image blocks with size of 14×14 on two databases by using SC. (a) UAV landform database 3, before feature sorting; (b) UAV landform database 3, after feature sorting; (c) UC Merced LU database, before feature sorting (d) UC Merced LU database, after feature sorting
    Training convergence curves for UC Merced LU database. (a) Training convergence curves obtained with four different methods; (b) training convergence curves obtained with SC-CNN algorithm
    Confusion matrix obtained by classify landforms with SC-CNN algorithm SC-CNN
    Classification effect maps of complex landforms image. (a) Landform image; (b) artificial landform division; (c) post-blocking image; (d) landform classification effect map
    • Table 1. Network structure of model based on SC and CNN

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      Table 1. Network structure of model based on SC and CNN

      LayerTypePatch sizeStrideZero paddingOutput size
      xInput256×256×3
      h1Convolution5×552128×128×64
      h2ReLU128×128×64
      h3Mean pooling3×3264×64×64
      h4Convolution3×32032×32×64
      h5ReLU32×32×64
      h6Max pooling3×3216×16×64
      h7Convolution7×71214×14×128
      h8ReLU14×14×128
      h9Max pooling3×327×7×128
      h10Convolution7×7101×1×128
      h11ReLU1×1×128
      h12Convolution1×1101×1×20
      oSVMv
    • Table 2. Classification accuracy of different algorithms on UC Merced LU database

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      Table 2. Classification accuracy of different algorithms on UC Merced LU database

      AlgorithmTraining accuracy /%Training time /h
      SVM78.570.7
      CS-CNN[12]92.864.5
      PSR[13]89.105.2
      MS-DCNN[11]91.345.9
      SC-CNN98.144.3
    • Table 3. Classification accuracy of existing methods on UAV landform database 3

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      Table 3. Classification accuracy of existing methods on UAV landform database 3

      AlgorithmTraining accuracy /%Training time /h
      SVM76.962.6
      CS-CNN[12]92.9112.9
      MS-DCNN[11]91.5313.7
      PCANet[14]86.4911.1
      SC-CNN97.5010.5
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    Fang Liu, Xin Wang, Lixia Lu, Guangwei Huang, Hongjuan Wang. Landform Image Classification Based on Sparse Coding and Convolutional Neural Network[J]. Acta Optica Sinica, 2019, 39(4): 0410001

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    Paper Information

    Category: Image Processing

    Received: Sep. 18, 2018

    Accepted: Dec. 12, 2018

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201939.0410001

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