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Volume 46 Issue 5
May  2020
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Journal of Beijing University of Aeronautics and Astronautics  > 2020  >  46(5): 994-1004.
LI Yong-yi, WANG Nan, WU Xin-yue, et al. Research on Non-Cooperative Automatic Docking Strategy of Umbilical Connector for Launch Vehicle under Fluctuating Wind Load[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0506(in Chinese)
Citation: ZHENG Lei, HU Weiduo, LIU Changet al. Large crater identification method based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 994-1004. doi: 10.13700/j.bh.1001-5965.2019.0342(in Chinese)
shu

Large crater identification method based on deep learning

doi: 10.13700/j.bh.1001-5965.2019.0342
  • 1.

    School of Astronautics, Beihang University, Beijing 100083, China

  • 2.

    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Funds:

National Natural Science Foundation of China 61703017

More Information
  • Corresponding author: HU Weiduo, E-mail:08109@buaa.edu.cn
  • Received Date: 28 Jun 2019
  • Accepted Date: 29 Sep 2019
  • Publish Date: 20 May 2020
    Abstract

  • Craters are the most significant topographic features on the surface of celestial bodies. The traditional method of craters identification is mainly to study the dichotomy of positive and negative samples of small craters, with low efficiency and accuracy. This paper takes large craters under the macroscopic view of the planet as the research object, combines the knowledge of digital image processing and neural network, creates a crater sample library of different data sources to study the influence of data source on network model generalization ability, and proposes a more efficient crater multi-classification identification method. Based on the Non-Maximum Suppression (NMS) algorithm, a higher precision crater detection algorithm is proposed. Through parameter optimization and experimental verification, the multi-scale and multi-classification craters automatic recognition network framework based on deep learning constructed in this paper achieves a high accuracy rate, with the recognition rate up to 0.985 on homologous verification set and 0.863 on heterogeneous verification set, and effectively improves the redundancy of detection box and false detection in target detection.

     

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  • [1]
    GROUP C A T W, ARVIDSON R E, BOYCE J, et al.Standard techniques for presentation and analysis of crater size-frequency data[J].Icarus, 1979, 37(2):467-474. doi: 10.1016/0019-1035(79)90009-5
    [2]
    PALAFOX L F, HAMILTON C W, SCHEIDT S P, et al.Automated detection of geological landforms on Mars using convolutional neural networks[J].Computers & Geosciences, 2017, 101:48-56.
    [3]
    CHENG Y, JOHNSON A E, MATTHIES L H, et al.Optical landmark detection for spacecraft navigation[C]//Proceedings of the 13th Annual AAS/AIAA Space Flight Mechanics Meeting, 2003: 1785-1803.
    [4]
    SAWABE Y, MATSUNAGA T, ROKUGAWA S.Automated detection and classification of lunar craters using multiple approaches[J].Advances in Space Research, 2006, 37(1):21-27. doi: 10.1016/j.asr.2005.08.022
    [5]
    KIM J R, MULLER J, VAN GASSELT S, et al.Automated crater detection, a new tool for Mars cartography and chronology[J].Photogrammetric Engineering & Remote Sensing, 2005, 71(10):1205-1217.
    [6]
    冯军华, 崔祜涛, 崔平远, 等.行星表面陨石坑检测与匹配方法[J].航空学报, 2010, 31(9):1858-1863.

    FENG J H, CUI H T, CUI P Y, et al.Autonomous crater detection and matching on planetary surface[J].Acta Aeronautica et Astronautica Sinica, 2010, 31(9):1858-1863(in Chinese).
    [7]
    DING M, CAO Y F, WU Q X.Method of passive image based crater autonomous detection[J].Chinese Journal of Aeronautics, 2009, 22(3):301-306. doi: 10.1016/S1000-9361(08)60103-X
    [8]
    CRACKNELL M J, READING A M.Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information[J].Computers & Geosciences, 2014, 63:22-33.
    [9]
    CHRISTOFF N, MANOLOVA A, JORDA L, et al.Morphological crater classification via convolutional neural network with application on MOLA data[C]//Advances in Neural Networks and Applications 2018, 2018: 1-5.
    [10]
    BARATA T, ALVES E I, SARAIVA J, et al.Automatic recognition of impact craters on the surface of Mars[C]//International Conference Image Analysis and Recognition.Berlin: Springer, 2004: 489-496.
    [11]
    BOUKERCHA A, AL-TAMEEMI A, GRUMPE A, et al.Automatic crater recognition using machine learning with different features and their combination[C]//Lunar and Planetary Science Conference, 2014, 45: 2842.
    [12]
    SILBURT A, ALI-DIB M, ZHU C, et al.Lunar crater identification via deep learning[J].Icarus, 2019, 317:27-38. doi: 10.1016/j.icarus.2018.06.022
    [13]
    WRIGHT E.SVS: Moon phase and libration, 2018[EB/OL].(2019-01-28)[2019-06-12].
    [14]
    HEAD J W, ADAMS J B, MCCORD T B, et al.Regional stratigraphy and geologic history of Mare Crisium[C]//Mare Crisium: The View From Luna 24, 1978: 43-74.
    [15]
    WATTERS T R, KONOPLIV A S.The topography and gravity of Mare Serenitatis:Implications for subsidence of the mare surface[J].Planetary and Space Science, 2001, 49(7):743-748. doi: 10.1016/S0032-0633(01)00007-1
    [16]
    SOLOMON S C, HEAD J W.Vertical movement in mare basins:Relation to mare emplacement, basin tectonics, and lunar thermal history[J].Journal of Geophysical Research: Solid Earth, 1979, 84(B4):1667-1682. doi: 10.1029/JB084iB04p01667
    [17]
    SCHULTZ P H, STAID M I, PIETERS C M.Lunar activity from recent gas release[J].Nature, 2006, 444(7116):184-186. doi: 10.1038/nature05303
    [18]
    KRVGER T, VAN DER BOGERT C H, HIESINGER H.Geomorphologic mapping of the lunar crater Tycho and its impact melt deposits[J].Icarus, 2016, 273:164-181. doi: 10.1016/j.icarus.2016.02.018
    [19]
    ZHANG K, ZUO W, CHEN Y, et al.Beyond a Gaussian denoiser:Residual learning of deep CNN for image denoising[J].IEEE Transactions on Image Processing, 2017, 26(7):3142-3155. doi: 10.1109/TIP.2017.2662206
    [20]
    DONG C, LOY C C, HE K, et al.Image super-resolution using deep convolutional networks[J].IEEE transactions on Pattern Analysis and Machine Intelligence, 2015, 38(2):295-307.
    [21]
    LONG J, SHELHAMER E, DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2015: 3431-3440.
    [22]
    MALTEZOS E, DOULAMIS N, DOULAMIS A, et al.Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds[J].Journal of Applied Remote Sensing, 2017, 11(4):42620.
    [23]
    ZHANG N, DONAHUE J, GIRSHICK R, et al.Part-based R-CNNs for fine-grained category detection[C]//European Conference on Computer Vision.Berlin: Springer, 2014: 834-849.
    [24]
    黄洁, 姜志国, 张浩鹏, 等.基于卷积神经网络的遥感图像舰船目标检测[J]北京航空航天大学学报, 2017, 43(9):1841-1848. doi: 10.13700/j.bh.1001-5965.2016.0755

    HUAGN J, JIANG Z G, ZHANG H P, et al.Ship object detection in remote sensing images using convolutional neural networks[J].Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(9):1841-1848(in Chinese). doi: 10.13700/j.bh.1001-5965.2016.0755
    [25]
    JIAO L, LIANG M, CHEN H, et al.Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10):5585-5599. doi: 10.1109/TGRS.2017.2710079
    [26]
    韩京冶, 许福, 陈志泊, 等.一种基于深度学习的交互式电话号码识别方法[J].北京航空航天大学学报, 2018, 44(5):1074-1080. doi: 10.13700/j.bh.1001-5965.2017.0357

    HAN J Y, XU F, CHEN Z B, et al.A deep learning based interactive recognition method for telephone numbers[J].Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5):1074-1080(in Chinese). doi: 10.13700/j.bh.1001-5965.2017.0357
    [27]
    KRIZHEVSKY A, SUTSKEVER I, HINTON G E.Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012: 1097-1105.
    [28]
    RIEDMILLER M, BRAUN H.A direct adaptive method for faster backpropagation learning: The RPROP algorithm[C]//Proceedings of the IEEE International Conference on Neural Networks.Piscataway: IEEE Press, 1993: 586-591.
    [29]
    ZEILER M D, FERGUS R.Visualizing and understanding convolutional networks[C]//European Conference on Computer Vision.Berlin: Springer, 2014: 818-833.
    [30]
    ZEILER M D.ADADELTA: An adaptive learning rate method[EB/OL].(2012-12-22)[2019-06-20].
    [31]
    KINGMA D P, BA J.Adam: A method for stochastic optimization[EB/OL].(2014-12-22)[2019-06-20].
    [32]
    TIELEMAN T, HINTON G.RMSPROP:Divide the gradient by a running average of its recent magnitude[J].Neural Networks for Machine Learning, 2012, 4:26-30.
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    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn

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