Acta Optica Sinica, Volume. 45, Issue 18, 1828017(2025)

Remote Sensing Image Simulation Method of Craters Based on Neural Radiance Fields (Invited)

Weichang Zhang, Xun Liu, Wei Li*, and Yongchao Zheng
Author Affiliations
  • Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China
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    Figures & Tables(10)
    Framework diagram of crater simulation method based on NeRF
    Principle of NeRF algorithm
    3D model of crater (left) and results by NeRF rendering (right)
    Principle of image harmony algorithm
    Directly synthesized image (top row) and image after image harmonization network (bottom row)
    Improving effectiveness of object detection
    • Table 1. Comparison of training effectiveness of YOLOv5 algorithm

      View table

      Table 1. Comparison of training effectiveness of YOLOv5 algorithm

      DatasetMethodPRF1mAP@0.5
      CE2Base0.6320.5890.6070.418
      Mix without harmonization0.6780.6060.6560.469
      Mix with harmonization0.6830.6350.6620.475
      LRO NACBase0.7240.3590.5370.246
      Mix without harmonization0.8240.2530.5350.254
      Mix with harmonization0.7390.4580.6070.300
    • Table 2. Comparison of training effectiveness of HRNet algorithm

      View table

      Table 2. Comparison of training effectiveness of HRNet algorithm

      DatasetMethodPRF1
      CE2Base0.9490.3110.434
      Mix without harmonization0.8410.3790.481
      Mix with harmonization0.7880.4780.552
      LRO NACBase0.6360.3910.464
      Mix without harmonization0.6700.4060.488
      Mix with harmonization0.6380.4210.494
    • Table 3. Comparison of training effectiveness of DeepMoon algorithm

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      Table 3. Comparison of training effectiveness of DeepMoon algorithm

      DatasetMethodPRF1
      CE2Base0.7840.5950.629
      Mix without harmonization0.8010.5860.642
      Mix with harmonization0.7880.5880.637
      LRO NACBase0.6260.5180.549
      Mix without harmonization0.6640.5940.612
      Mix with harmonization0.6580.5900.607
    • Table 4. Comparison of image synthesis algorithms training effectiveness

      View table

      Table 4. Comparison of image synthesis algorithms training effectiveness

      AlgorithmPRF1mAP@0.5
      YOLOv5Our method0.6830.6350.6620.475
      Deep image blending0.5990.6260.6170.416
      DoveNet0.6520.6680.6660.466
      BargainNet0.6920.6200.6620.457
      HRNetOur method0.7880.4780.552/
      Deep image blending0.9760.2710.400/
      DoveNet0.8950.2520.376/
      BargainNet0.8810.2410.367/
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    Weichang Zhang, Xun Liu, Wei Li, Yongchao Zheng. Remote Sensing Image Simulation Method of Craters Based on Neural Radiance Fields (Invited)[J]. Acta Optica Sinica, 2025, 45(18): 1828017

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: May. 30, 2025

    Accepted: Jul. 30, 2025

    Published Online: Sep. 15, 2025

    The Author Email: Wei Li (wei_li_bj@163.com)

    DOI:10.3788/AOS251184

    CSTR:32393.14.AOS251184

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