• Acta Optica Sinica
  • Vol. 37, Issue 11, 1129001 (2017)
Chenghao Liu1、*, Zhi Li2, Can Xu3, and Qichen Tian1
Author Affiliations
  • 1 Department of Graduate Management, Equipment Academy, Beijing 101416, China
  • 2 Department of Space Command, Equipment Academy, Beijing 101416, China
  • 3 Department of Space Equipment, Equipment Academy, Beijing 101416, China
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    DOI: 10.3788/AOS201737.1129001 Cite this Article Set citation alerts
    Chenghao Liu, Zhi Li, Can Xu, Qichen Tian. BRDF Model for Commonly Used Materials of Space Targets Based on Deep Neural Network[J]. Acta Optica Sinica, 2017, 37(11): 1129001 Copy Citation Text show less
    Angles schematic in BRDF measurement
    Fig. 1. Angles schematic in BRDF measurement
    Activation effect of ReLU. (a) Before activation; (b) after activation
    Fig. 2. Activation effect of ReLU. (a) Before activation; (b) after activation
    Dropout diagram
    Fig. 3. Dropout diagram
    Modeling process of DNN
    Fig. 4. Modeling process of DNN
    Fitting effect of anode plate coated by gold film. (a) θi=2°; (b) θi=15°; (c) θi=60°; (d) θi=75°
    Fig. 5. Fitting effect of anode plate coated by gold film. (a) θi=2°; (b) θi=15°; (c) θi=60°; (d) θi=75°
    Loss function value decline curve of anode plate coated by gold film
    Fig. 6. Loss function value decline curve of anode plate coated by gold film
    Fitting effect of epoxy paint. (a) θi=2°; (b) θi=30°; (c) θi=75°; (d) θi=80°
    Fig. 7. Fitting effect of epoxy paint. (a) θi=2°; (b) θi=30°; (c) θi=75°; (d) θi=80°
    Loss function value decline curve of epoxy paint
    Fig. 8. Loss function value decline curve of epoxy paint
    Comparison of fitting effects of three models for epoxy paint. DNN model: (a) θi=5°, (b) θi=30°; five-parameter model: (c) θi=5°, (d) θi=30°; Phong model: (e) θi=5°, (f) θi=30°
    Fig. 9. Comparison of fitting effects of three models for epoxy paint. DNN model: (a) θi=5°, (b) θi=30°; five-parameter model: (c) θi=5°, (d) θi=30°; Phong model: (e) θi=5°, (f) θi=30°
    Comparison of fitting effects of three models for polyimide film. DNN model: (a) θi=5°, (b) θi=30°; five-parameter model: (c) θi=5°, (d) θi=30°; Phong model: (e) θi=5°, (f) θi=30°
    Fig. 10. Comparison of fitting effects of three models for polyimide film. DNN model: (a) θi=5°, (b) θi=30°; five-parameter model: (c) θi=5°, (d) θi=30°; Phong model: (e) θi=5°, (f) θi=30°
    Training errorTest errorAverage test error
    0.007410.015710.02279
    0.030360.02320
    0.031450.01546
    0.042370.06529
    0.018710.01355
    0.018680.01634
    0.076840.03029
    0.023920.01927
    0.018390.01293
    0.016270.01586
    Table 1. Modeling errors of 2A12 white paint
    Ratio of training sampleTraining errorTest errorAverage test error
    0.750.018990.018360.01715
    0.034210.02942
    0.016000.01036
    0.026010.02160
    0.006470.00600
    0.500.025000.020380.01664
    0.004970.00541
    0.008780.01324
    0.020460.01847
    0.015930.02227
    Table 2. Modeling errors comparison of training samples with different scales
    Chenghao Liu, Zhi Li, Can Xu, Qichen Tian. BRDF Model for Commonly Used Materials of Space Targets Based on Deep Neural Network[J]. Acta Optica Sinica, 2017, 37(11): 1129001
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