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Advances in planetary target detection and classification using remote sensing data

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：dikc@radi.ac.cn
  • Introduction：邸凯昌，1967年生，男，研究员，研究方向为行星遥感制图与导航定位。E-mail: dikc@radi.ac.cn
DI Kaichang1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

YE Lejia12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

WANG Runzhi12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：wangyx716@radi.ac.cn
  • Introduction：王晔昕，1986年生，女，副研究员，研究方向为行星遥感图像处理与模式识别。E-mail:wangyx716@radi.ac.cn
WANG Yexin1*

resumen

Planetary remote sensing images are an important data source for planetary observations and are the basis for qualitative and quantitative analysis of the planet’s surface. Analyzing the features of the planet’s surface based on remote sensing images and recognizing and classifying topographic features from massive planetary remote sensing data are significant fundamental tasks in planetary science research. In this new era for deep space exploration and development, multiple missions from different countries and agencies are being implemented. Accordingly, enormous amount of data will be obtained, and this situation requires using automatic target recognition and terrain classification technologies. This study systematically reviews and summarizes the research progress and advances of topography and landform recognition and classification technologies using planetary image data since the start of lunar and deep space exploration missions. First, the moon, Mars, and other planetary exploration missions and the acquired image data are briefly described. After a short introduction to the research progress of general target recognition and classification techniques, the applications of these techniques using the image data of the moon, Mars, and other planets are then elaborated as follows. (1) For lunar images, review of target recognition and classification progress is detailed in three aspects: recognition of the circular structure (i. e., crater), recognition of linear structure (e.g., wrinkle ridge), and terrain classification of the lunar surface. (2) For Mars images, the detailed advances including recognition of tectonic (e. g., crater and volcano), aeolian (e.g., slope streak, sand dune, and dust devil track), fluvial landforms (e.g., channel and gully), and other features (e.g., rock), as well as terrain classification of the Martian surface, are elaborated. (3) For target recognition and classification from other planetary images, the study introduces the research advances on other terrestrial planets (e.g., Mercury and Venus) in the solar system and asteroids that have been explored. Specifically, the asteroid parts are elaborated according to different exploration approaches: close flyby, orbiting, anchoring, and sample acquisition. Finally, future research directions of target recognition and classification using planetary image data are discussed. The future research directions include (1) target recognition and classification using multi-source data: data from different types of sensors, data of different resolutions, and data from different platforms and time; (2) automatic recognition and classification using unsupervised approach; and (3) multi-task image intelligence applications. Achieving high-precision automatic recognition and classification of the planetary surface is still challenging because of the complex environment and featureless texture of the planetary surface. In the future, automatic recognition and classification will surely play increasingly important roles in supporting planetary exploration engineering missions and scientific research through the continuous improvement in data quality and development of related field technologies.

palabra clave

planetary exploration;remote sensing data;target recognition;terrain classification;machine learning

References

  1. 1.
    Adams R and Bischof L. 1994. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6): 641-647
  2. 2.
    Bandeira L, Ding W and Stepinski T F. 2012. Detection of sub-kilometer craters in high resolution planetary images using shape and texture features. Advances in Space Research, 49(1): 64-74
  3. 3.
    Bandeira L, Marques J S, Saraiva J and Pina P. 2011. Automated detection of Martian dune fields. IEEE Geoscience and Remote Sensing Letters, 8(4): 626-630
  4. 4.
    Bandeira L, Marques J S, Saraiva J and Pina P. 2013. Advances in automated detection of sand dunes on Mars. Earth Surface Processes and Landforms, 38(3): 275-283
  5. 5.
    Barata M T, Lopes F C, Pina P, Alves E I and Saraiva J. 2015. Automatic detection of wrinkle ridges in Venusian Magellan imagery. Geological Society, London, Special Publications, 401(1): 357-376
  6. 6.
    Barlow N G. 1988. Crater size-frequency distributions and a revised Martian relative chronology. Icarus, 75(2): 285-305
  7. 7.
    Barlow N G. 2000. Updates to the “catalog of large Martian impact craters”//Proceedings of the 31st Annual Lunar and Planetary Science Conference. Houston, Texas: [s.n.]
  8. 8.
    Barnouin O S, Zuber M T, Smith D E, Neumann G A, Herrick R R, Chappelow J E, Murchie S L and Prockter L M. 2012. The morphology of craters on Mercury: results from messenger flybys. Icarus, 219(1): 414-427
  9. 9.
    Barucci M A, Fulchignoni M, Ji J, Marchi S and Thomas N. 2015. The flybys of asteroids (2867) Šteins, (21) Lutetia, and (4179) Toutatis//Michel P, DeMeo F E and Bottke W F, eds. Asteroids IV. Tucson: The University of Arizona Press: 433-450
  10. 10.
    Bergonio J R, Rottas K M and Schorghofer N. 2013. Properties of martian slope streak populations. Icarus, 225(1): 194-199
  11. 11.
    Bue B D and Stepinski T F. 2006. Automated classification of landforms on Mars. Computers and Geosciences, 32(5): 604-614
  12. 12.
    Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6): 679-698
  13. 13.
    Carrera D, Bandeira L, Santana R and Lozano J A. 2019. Detection of sand dunes on Mars using a regular vine-based classification approach. Knowledge-Based Systems, 163: 858-874
  14. 14.
    Castelvecchi D. 2018. Daring Japanese mission reaches unexplored asteroid Ryugu. Nature, 558(7711): 495-496
  15. 15.
    Chang Y R and Li Z K. 2016. A lunar terrain auto recognition algorithm by gushing and immersion//Proceedings of 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering. Wuhan: Atlantis Press
  16. 16.
    Chen J H. 2016. A lunar terrain auto recognition algorithm with adaptive threshold by gushing and immersion//Proceedings of 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer. Tianjin: Atlantis Press
  17. 17.
    Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2018a. DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE transactions on pattern analysis and machine intelligence, 40(4): 834-848
  18. 18.
    Chen M, Liu D Y, Qian K J, Li J, Lei M L and Zhou Y. 2018b. Lunar crater detection based on terrain analysis and mathematical morphology methods using digital elevation models. IEEE Transactions on Geoscience and Remote Sensing, 56(7): 3681-3692
  19. 19.
    Cheng H Z, Sun F T, Buthpitiya S, Zhang Y and Nefian A V. 2010. Lunar image classification for terrain detection//Proceedings of the 6th International Symposium on Visual Computing. Las Vegas, NV, USA: Springer: 1-8
  20. 20.
    Chuang F C, Beyer R A, McEwen A S and Thomson B J. 2007. HiRISE observations of slope streaks on Mars. Geophysical Research Letters, 34(20): L20204
  21. 21.
    Cohen J P. 2016. Automated Crater Detection Using Machine Learning. Boston: University of Massachusetts Boston
  22. 22.
    Cohen J P and Ding W. 2014. Crater detection via genetic search methods to reduce image features. Advances in Space Research, 53(12): 1768-1782
  23. 23.
    Coleman G B and Andrews H C. 1979. Image segmentation by clustering. Proceedings of the IEEE, 67(5): 773-785
  24. 24.
    Cortes C and Vapnik V. 1995. Support-vector networks. Machine Learning, 20(3): 273-297
  25. 25.
    Da Silva E, Puga F, Casaca W, Cruz B and Negri R. 2018. Slope streaks segmentation using wave atoms and morphological operators//Proceedings of the 42nd COSPAR Scientific Assembly. Pasadena, California: [s.
  26. 26.
    Dalal N and Triggs B. 2005. Histograms of oriented gradients for human detection//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA: IEEE: 886-893
  27. 27.
    DeLatte D M, Crites S T, Guttenberg N, Tasker E J and Yairi T. 2019a. Segmentation convolutional neural networks for automatic crater detection on Mars. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(8): 2944-2957
  28. 28.
    DeLatte D M, Crites S T, Guttenberg N and Yairi T. 2019b. Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era. Advances in Space Research, 64(8): 1615-1628
  29. 29.
    Di K C, Li W, Yue Z Y, Sun Y W and Liu Y L. 2014. A machine learning approach to crater detection from topographic data. Advances in Space Research, 54(11): 2419-2429
  30. 30.
    Di K C, Liu B and Liu Z Q. 2018. Review and prospect of Mars mapping technique using remote sensing data. Spacecraft Engineering, 27(1): 10-24
  31. 31.
    Di K C, Liu B, Liu Z Q and Zou Y L. 2016. Review and prospect of lunar mapping using remote sensing data. Journal of Remote Sensing, 20(5): 1230-1242
  32. 32.
    Di K C, Liu Z Q, Wan W H and Peng M. 2015. Remote Sensing Mapping and rover Navigation for Lunar and Mars. Beijing: Science Press
  33. 33.
    Di K C, Yue Z Y, Liu Z Q and Wang S L. 2013. Automated rock detection and shape analysis from mars rover imagery and 3D point cloud data. Journal of Earth Science, 24(1): 125-135
  34. 34.
    Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236-256
  35. 35.
    Dunlop H, Thompson D R and Wettergreen D. 2007. Multi-scale features for detection and segmentation of rocks in mars images//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, United States: IEEE: 1-7
  36. 36.
    Fang T, Huo H and Ma H P. 2016. Intelligent Interpretation of High-Resolution Remote Sensing Images. Beijing: Science Press
  37. 37.
    Foody G M and Mathur A. 2004. A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(6): 1335-1343
  38. 38.
    Friedl M A and Brodley C E. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3): 399-409
  39. 39.
    Fujiwara A, Kawaguchi J, Yeomans D K, Abe M, Mukai T, Okada T, Saito J, Yano H, Yoshikawa M, Scheeres D J, Barnouin-Jha O, Cheng A F, Demura H, Gaskell R W, Hirata N, Ikeda H, Kominato T, Miyamoto H, Nakamura A M, Nakamura R, Sasaki S and Uesugi K. 2006. The rubble-pile Asteroid itokawa as observed by Hayabusa. Science, 312(5778): 1330-1334
  40. 40.
    Fukushima K. 1988. Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks, 1(2): 119-130
  41. 41.
    Gal-Edd J and Cheuvront A. 2015. The OSIRIS-REx Asteroid Sample Return Mission operations design//Proceedings of 2015 IEEE Aerospace Conference. Big Sky, MT, USA: IEEE: 1-9
  42. 42.
    Gao Y, Spiteri C, Pham M T and Al-Milli S. 2014. A survey on recent object detection techniques useful for monocular vision-based planetary terrain classification. Robotics and Autonomous Systems, 62(2): 151-167
  43. 43.
    Glaude Q. 2017. CraterNet: A Fully Convolutional Neural Network for Lunar Crater Detection Based on Remotely Sensed Data. Belgium: University of Liège
  44. 44.
    Goodfellow I, Bengio Y and Courville A. 2016. Deep learning. Cambridge MA: MIT Press
  45. 45.
    Gou S, Yue Z Y, Di K C and Liu Z Q. 2018. A global catalogue of Ceres impact craters ≥ 1 km and preliminary analysis. Icarus, 302: 296-307
  46. 46.
    Haris K, Efstratiadis S N, Maglaveras N and Katsaggelos A K. 1998. Hybrid image segmentation using watersheds and fast region merging. IEEE Transactions on Image Processing, 7(12): 1684-1699
  47. 47.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, United States: IEEE: 770-778
  48. 48.
    Hinton G E, Osindero S and Teh Y W. 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18(7): 1527-1554
  49. 49.
    Huang J C, Ji J H, Ye P J, Wang X L, Yan J, Meng L Z, Wang S, Li C L, Li Y, Qiao D, Zhao W, Zhao Y H, Zhang T X, Liu P, Jiang Y, Rao W, Li S, Huang C N, Ip WH, Hu S C, Zhu M H, Yu L L, Zou Y L, Tang X L, Li J Y, Zhao H B, Huang H, Jiang X J and Bai J M. 2013. The Ginger-shaped asteroid 4179 Toutatis: new observations from a successful flyby of Chang’E-2. Scientific Reports, 3: 3411
  50. 50.
    Ida T and Sambonsugi Y. 1998. Image segmentation and contour detection using fractal coding. IEEE Transactions on Circuits and Systems for Video Technology, 8(8): 968-975
  51. 51.
    Isola P, Zhu J Y, Zhou T H and Efros A A. 2017. Image-to-image translation with conditional adversarial networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE: 5967-5976
  52. 52.
    Itti L, Koch C and Niebur E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11): 1254-1259
  53. 53.
    Jiang H K, Tian X L and Xu A A. 2015. A new segmentation algorithm for lunar surface terrain based on CCD images. Research in Astronomy and Astrophysics, 15(9): 1604-1612
  54. 54.
    Jin S G and Zhang T Y. 2014. Automatic detection of impact craters on Mars using a modified adaboosting method. Planetary and Space Science, 99: 112-117
  55. 55.
    Kim J R, Muller J P, Van Gasselt S, Morley J G and Neukum G. 2005. Automated crater detection, a new tool for mars cartography and chronology. Photogrammetric Engineering and Remote Sensing, 71(10): 1205-1217
  56. 56.
    Kinser R M, Gibbs V B and Barlow N G. 2013. A new database of craters 5-km-diameter and larger for the moon: western Nearside//Proceedings of the 44th Lunar and Planetary Science Conference. The Woodlands, Texas: [s.
  57. 57.
    Kreslavsky M A and Basilevsky A T. 1998. Morphometry of wrinkle ridges on Venus: comparison with other planets. Journal of Geophysical Research: Planets, 103(E5): 11103-11111
  58. 58.
    Krizhevsky A, Sutskever I, Hinton G. E. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems. 1097-1105.
  59. 59.
    Krüger T, Hergarten S and Kenkmann T. 2018. Deriving morphometric parameters and the simple-to-complex transition diameter from a high‐resolution, global database of fresh lunar impact craters (D≥~3 km). Journal of Geophysical Research: Planets, 123(10): 2667-2690
  60. 60.
    LeCun Y, Boser B, Denker J S, Henderson D, Howard R E, Hubbard W and Jackel L D. 1989. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4): 541-551
  61. 61.
    LeCun Y, Bottou L, Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278-2324
  62. 62.
    Lee C. 2019. Automated crater detection on Mars using deep learning. Planetary and Space Science, 170: 16-28
  63. 63.
    Li B, Ling Z C, Zhang J, Chen J, Ni Y H and Liu C L. 2018a. Displacement-length ratios and contractional strains of lunar wrinkle ridges in Mare Serenitatis and Mare Tranquillitatis. Journal of Structural Geology, 109: 27-37
  64. 64.
    Li C L, Liu J J, Yan W, Feng J Q, Ren X and Liu B. 2019. Overview of scientific objectives for minor planets exploration. Journal of Deep Space Exploration, 6(5): 424-436
  65. 65.
    Li G Q, Geng Y H and Xiao X M. 2018b. Multi-scale rock detection on Mars. Science China Information Sciences, 61(10): 102301
  66. 66.
    Li H and Zhong C. 2013. Automatic crater detection with laser altimetric data. Earth Science—Journal of China University of Geosciences, 38(S1): 161-166
  67. 67.
    Li J, Chen J P, Wang N and He S J. 2014. A new automated approach to detecting and extracting the linear structures on the lunar surface: a case study on the lunar mare ridge of Mare Serenitatis. Earth Science Frontiers, 21(6): 223-228
  68. 68.
    Li K, Mu L L, Liu J J, Li C L and Qin Q Q. 2011. Impact crater detection based on regional segmentation using Chang'E-1 CCD data//Proceedings of 2011 IEEE 4th International Congress on Image and Signal Processing. Shanghai, China: IEEE: 1911-1915
  69. 69.
    Li W, Di K C, Yue Z Y, Liu Y L and Sun S J. 2015. Automated detection of Martian gullies from HiRISE imagery. Photogrammetric Engineering and Remote Sensing, 81(12): 913-920
  70. 70.
    Li Z K, Chang Y R, Chen J H and Tian X L. 2017. A new iterative auto-recognition algorithm for lunar terrain. Journal of Astronautics, 38(1): 72-79
  71. 71.
    Li Z K, Chen J H, Chang Y R and Tian X L. 2016. An improved recognition algorithm for lunar terrain based on CCD image//Proceedings of the 4th International Conference on Machinery, Materials and Computing Technology. Hangzhou: Atlantis Press: 2352-5401
  72. 72.
    Lienhart R and Maydt J. 2002. An extended set of Haar-like features for rapid object detection//Proceedings of the International Conference on Image Processing. Rochester, NY, United States: IEEE
  73. 73.
    Liu A, Zhou D H and Chen M Y. 2016. A robust crater detection and recognition method based on blocked principal components analysis. Journal of Beijing University of Posts and Telecommunications, 39(1): 63-67
  74. 74.
    Liu Y X, Li C L and Liu J J. 2018. Automatic small crater recognition using digital elevation model from Chang'E-2 by contour line. Astronomical Research and Technology, 15(4): 479-486
  75. 75.
    Liu Y X, Liu J J, Mu L L and Li C L. 2012. A review of impact-crater detection. Astronomical Research and Technology, 9(2): 203-212
  76. 76.
    Liu Z Q, Yue Z Y, Michael G, Gou S, Di K C, Sun S J and Liu J Z. 2018. A global database and statistical analyses of (4) Vesta craters. Icarus, 311: 242-257
  77. 77.
    Lo H Z. 2016. Deep Networks: Applications, Interpretability, and Optimization. Boston: University of Massachusetts Boston
  78. 78.
    Losiak A, Wilhelms D E, Byrne C J, Thaisen K G, Weider S Z, Kohout T, O'Sullivan K and Kring D A. 2009. A new lunar impact crater database//Proceedings of the 40th Lunar and Planetary Science Conference. The Woodlands, Texas: [s.n.]
  79. 79.
    Lou Y L and Kang Z Z. 2018. Extract the lunar linear structure information by average filtering method based on DEM data. Science of Surveying and Mapping, 43(5): 155-160
  80. 80.
    Luo Z F, Kang Z Z and Liu X Y. 2014. The automatic extraction and recognition of lunar impact craters fusing CCD images and DEM data of Chang'e-1. Acta Geodaetica et Cartographica Sinica, 43(9): 924-930
  81. 81.
    Maeda K, Ogawa T and Haseyama M. 2015. Automatic detection of Martian dust storms from heterogeneous data based on decision level fusion//Proceedings of 2015 IEEE International Conference on Image Processing. Quebec City, Canada: IEEE: 2246-2250
  82. 82.
    Malladi R, Sethian J A and Vemuri B C. 1995. Shape modeling with front propagation: a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2): 158-175
  83. 83.
    Maren A J. 1990. Multilayer cooperative/competitive networks//Maren A J, Harston C T and Pap R M, eds. Handbook of Neural Computing Applications. San Diego: Academic Press: 179-202
  84. 84.
    Martins R, Pina P, Marques J S and Silveira M. 2009. Crater detection by a boosting approach. IEEE Geoscience and Remote Sensing Letters, 6(1): 127-131
  85. 85.
    Meng D, Cao Y F and Wu Q X. 2013. Novel approach of crater detection by crater candidate region selection and matrix-pattern-oriented least squares support vector machine. Chinese Journal of Aeronautics, 26(2): 385-393
  86. 86.
    Micheal A A, Vani K and Sanjeevi S. 2014. Automatic detection of ridges in lunar images using phase symmetry and phase congruency. Computers and Geosciences, 73: 122-131
  87. 87.
    Molloy I and Stepinski T F. 2007. Automatic mapping of valley networks on Mars. Computers and Geosciences, 33(6): 728-738
  88. 88.
    Nogrady B. 2018. Japanese rover lands on ancient asteroid for 16-hour mission. Nature
  89. 89.
    Ono M, Fuchs T J, Steffy A, Maimone M and Yen J. 2015. Risk-aware planetary rover operation: autonomous terrain classification and path planning//Proceedings of 2015 IEEE Aerospace Conference. Big Sky, MT, United States: IEEE: 1-10
  90. 90.
    Ono M, Heverly M, Rothrock B, Ishimatsu T, Almeida E, Calef F, Soliman T, Williams N, Gengl H, Nicholas A, Stilley E K, Otsu K, Trautman M, Lange R and Milkovich S. 2018. Mars 2020 surface mission performance analysis: part 2. Surface traversability//Proceedings of 2018 AIAA SPACE and Astronautics Forum and Exposition. Orlando, FL, United States: AIAA
  91. 91.
    Ono M, Rothrock B, Almeida E, Ansar A, Otero R, Huertas A and Heverly M. 2016. Data-driven surface traversability analysis for Mars 2020 landing site selection//Proceedings of 2016 IEEE Aerospace Conference. Big Sky, MT, USA: IEEE: 1-12
  92. 92.
    Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66
  93. 93.
    Ouyang Z Y. 2005. Introduction to Lunar science. Beijing: China Astronautic Publishing House
  94. 94.
    Palafox L F, Hamilton C W, Scheidt S P and Alvarez A M. 2017. Automated detection of geological landforms on Mars using Convolutional Neural Networks. Computers and Geosciences, 101: 48-56
  95. 95.
    Papageorgiou C P, Oren M and Poggio T. 1998. A general framework for object detection//Proceedings of IEEE Sixth International Conference on Computer Vision. Bombay, India: 555-562
  96. 96.
    Peng M, Wang Y, Yue Z and Di K. 2019. Automated detection of lunar ridges based on Dem data//Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Enschede, The Netherlands: [s.
  97. 97.
    Preusker F, Scholten F, Matz K D, Roatsch T, Jaumann R, Raymond C A and Russell C T. 2014. Global shape of (4) Vesta from Dawn FC stereo images//Proceedings of LPI Contributions. Houston, Texas: [s.
  98. 98.
    Rayman M D, Varghese P, Lehman D H and Livesay L L. 2000. Results from the Deep Space 1 technology validation mission. Acta Astronautica, 47(2-9): 475-487
  99. 99.
    Robbins S J. 2019. A new global database of lunar impact craters >1-2 km: 1. Crater locations and sizes, comparisons with published databases, and global analysis. Journal of Geophysical Research: Planets, 124(4): 871-892
  100. 100.
    Robbins S J and Hynek B M. 2012a. A new global database of Mars impact craters ≥1 km: 1. Database creation, properties, and parameters. Journal of Geophysical Research: Planets, 117(E5): E05004
  101. 101.
    Robbins S J and Hynek B M. 2012b. A new global database of Mars impact craters ≥1 km: 2. Global crater properties and regional variations of the simple-to-complex transition diameter. Journal of Geophysical Research: Planets, 117(E6): E06001
  102. 102.
    Rothrock B, Papon J, Kennedy R, Ono M and Heverly M. 2016. SPOC: deep learning-based terrain classification for mars rover missions//Proceedings of AIAA SPACE Conferences and Exposition. Long Beach, CA, United States: AIAA: 2016-5539
  103. 103.
    Salamunićcar G, Lončarić S and Mazarico E. 2012. LU60645GT and MA132843GT catalogues of Lunar and Martian impact craters developed using a Crater Shape-based interpolation crater detection algorithm for topography data. Planetary and Space Science, 60(1): 236-247
  104. 104.
    Salamunićcar G, Lončarić S, Pina P, Bandeira L and Saraiva J. 2011. MA130301GT catalogue of Martian impact craters and advanced evaluation of crater detection algorithms using diverse topography and image datasets. Planetary and Space Science, 59(1): 111-131
  105. 105.
    Saraiva J, Bandeira L P C and Pina P. 2006. A structured approach to automated crater detection//Proceedings of the 37th Annual Lunar and Planetary Science Conference. League City, Texas: [s.n.]
  106. 106.
    Schaber G G, Strom R G, Moore H J, Soderblom L A, Kirk R L, Chadwick D J, Dawson D D, Gaddis L R, Boyce J M and Russell J. 1992. Geology and distribution of impact craters on Venus: what are they telling us? Journal of Geophysical Research: Planets, 97(E8): 13257-13301
  107. 107.
    Scheidt S P, Palafox L F, Hamilton C W and Zimbelman J R. 2015. Automated detection of transverse Aeolian ridges on mars using convolutional neural networks and a field-based terrestrial orthoimage training set//Proceedings of the Fourth Annual International Planetary Dunes Workshop. Boise, Idaho: [s.
  108. 108.
    Schorghofer N, Aharonson O, Gerstell M F and Tatsumi L. 2007. Three decades of slope streak activity on Mars. Icarus, 191(1): 132-140
  109. 109.
    Schweitzer H, Bell J W and Wu F. 2002. Very fast template matching//Proceedings of the 7th European Conference on Computer Vision. Copenhagen, Denmark: Springer: 358-372
  110. 110.
    Schwenk H and Bengio Y. 2000. Boosting neural networks. Neural Computation, 12(8): 1869-1887
  111. 111.
    Shang C J and Barnes D. 2013. Fuzzy-rough feature selection aided support vector machines for Mars image classification. Computer Vision and Image Understanding, 117(3): 202-213
  112. 112.
    Silburt A, Ali-Dib M, Zhu C C, Jackson A, Valencia D, Kissin Y, Tamayo D and Menou K. 2019. Lunar crater identification via deep learning. Icarus, 317: 27-38
  113. 113.
    Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  114. 114.
    Statella T, Pina P and Da Silva E A. 2012. Image processing algorithm for the identification of Martian dust devil tracks in MOC and HiRISE images. Planetary and Space Science, 70(1): 46-58
  115. 115.
    Stepinski T F and Bagaria C. 2009. Segmentation-based unsupervised terrain classification for generation of physiographic maps. IEEE Geoscience and Remote Sensing Letters, 6(4): 733-737
  116. 116.
    Stepinski T F and Collier M L. 2004. Extraction of Martian valley networks from digital topography. Journal of Geophysical Research: Planets, 109 (E11): E11005
  117. 117.
    Stepinski T F, Ding W and Vilalta R. 2012. Detecting impact craters in planetary images using machine Learning//Magdalena-Benedito R, Martínez-Sober M, Martínez-Martínez J M, Vila-Francés J and Escandell-Montero P, eds. Intelligent Data Analysis for Real-Life Applications: Theory and Practice. IGI Global, Hershey, PA, United States: 146-159
  118. 118.
    Strom R G. 1964. Analysis of lunar lineaments, I: tectonic maps of the moon. Communications of the Lunar and Planetary Laboratory, 2: 205-216
  119. 119.
    Stutz D, Hermans A and Leibe B. 2018. Superpixels: an evaluation of the state-of-the-art. Computer Vision and Image Understanding, 166: 1-27
  120. 120.
    Svetnik V, Liaw A, Tong C, Christopher Culberson J, Sheridan R P and Feuston B P. 2003. Random forest: a classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences, 43(6): 1947-1958
  121. 121.
    Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, United States: IEEE: 1-9
  122. 122.
    Tamililakkiya V, Vani K, Lavanya A and Micheal A. 2011. Linear and non-linear feature extraction algorithms for lunar images. Signal and Image Processing: An International Journal, 2(4): 161-172
  123. 123.
    Tan X Y and Triggs B. 2007. Fusing Gabor and LBP feature sets for kernel-based face recognition//Proceedings of the 3rd International Workshop on Analysis and Modeling of Faces and Gestures. Rio de Janeiro, Brazil: Springer: 235-249
  124. 124.
    Tanaka K L, Schaber G G, Chapman M G, Stofan E R, Campbell D B, Davis P A, Guest J E, Mcgill G E, Rogers P G, Saunders R S and Zimbelman J R. 1994. The Venus geologic mappers' handbook. U.S. technical report of U.S. Geological Survey: USGS OFR 93-516
  125. 125.
    Thompson D R and Castano R. 2007. Performance comparison of rock detection algorithms for autonomous planetary geology//Proceedings of 2007 IEEE Aerospace Conference. Big Sky, MT, United states: IEEE: 1-9
  126. 126.
    Unser M. 1995. Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing, 4(11): 1549-1560
  127. 127.
    Urbach E R and Stepinski T F. 2009. Automatic detection of sub-km craters in high resolution planetary images. Planetary and Space Science, 57(7): 880-887
  128. 128.
    Vaz D A, Sarmento P T K, Barata M T, Fenton L K and Michaels T I. 2015. Object-based dune analysis: automated dune mapping and pattern characterization for Ganges Chasma and Gale crater, Mars. Geomorphology, 250: 128-139
  129. 129.
    Veverka J, Belton M, Klaasen K and Chapman C. 1994. Galileo's encounter with 951 Gaspra: overview. Icarus, 107(1): 2-17
  130. 130.
    Vinogradova T, Burl M and Mjolsness E. 2002. Training of a crater detection algorithm for Mars crater imagery//Proceedings of the IEEE Aerospace Conference. Big Sky, MT, United States: IEEE
  131. 131.
    Wagstaff K L, Panetta J, Ansar A, Greeley R, Hoffer M P, Bunte M and Schörghofer N. 2012. Dynamic landmarking for surface feature identification and change detection. ACM Transactions on Intelligent Systems and Technology, 3(3): 49
  132. 132.
    Wang C Z, Tang G A, Yuan S, Sun J W and Liu K. 2015. A method for identifying the lunar morphology based on texture from DEMs. Journal of Geo-information Science, 17(1): 45-53
  133. 133.
    Wang D, Xing S, Xu Q, Geng X and Shi Q S. 2015a. Methodology of automatic crater detection based on deep space planetary 3D profile. Journal of Geomatics Science and Technology, 32(6): 619-625
  134. 134.
    Wang D, Xu Q, Xing S and Liu Z R. 2015b. Analysis and description of the asteroid topography features. Journal of Deep Space Exploration, 2(4): 358-364
  135. 135.
    Wang H, Chen J S and Yu X M. 2013. Feature selection and its application in object-oriented classification. Journal of Remote Sensing, 17(4): 816-829
  136. 136.
    Wang H, Jiang J and Zhang G J. 2018. CraterIDNet: an end-to-end fully convolutional neural network for crater detection and identification in remotely sensed planetary images. Remote Sensing, 10(7): 1067
  137. 137.
    Wang J, Cheng W M, Zhou C H and Zhao M. 2014. Identification and morphologic expression of Lunar impact craters. Geographical Research, 33(7): 1251-1263
  138. 138.
    Wang X Y, Han T X and Yan S C. 2009. An HOG-LBP human detector with partial occlusion handling//Proceedings of IEEE 12th International Conference on Computer Vision. Kyoto, Japan: IEEE: 32-39
  139. 139.
    Wang Y, Yang G and Guo L. 2015. A novel sparse boosting method for crater detection in the high resolution planetary image. Advances in Space Research, 56(5): 982-991
  140. 140.
    Wang Y R and Wu B. 2019. Active machine learning approach for crater detection from planetary imagery and digital elevation models. IEEE Transactions on Geoscience and Remote Sensing, 57(8): 5777-5789
  141. 141.
    Wang Y X, Di K C, Xin X and Wan W H. 2017. Automatic detection of Martian dark slope streaks by machine learning using HiRISE images. ISPRS Journal of Photogrammetry and Remote Sensing, 129: 12-20
  142. 142.
    Watanabe S I, Tsuda Y, Yoshikawa M, Tanaka S, Saiki T and Nakazawa S. 2017. Hayabusa2 mission overview. Space Science Reviews, 208(1-4): 3-16
  143. 143.
    Wilhelms D. 1990. Geologic mapping//Greeley R and Batson R, eds. Planetary Mapping. Cambridge, UK: Cambridge University Press
  144. 144.
    Wu W R, Dong G L and Li H T. 2013. Engineering and Technology of Deep Space TT and C System. Beijing: Science Press
  145. 145.
    Wu W R, Liu J Z, Tang Y H, Yu D Y, Yu G B and Zhang Z. 2019. China lunar exploration program. Journal of Deep Space Exploration, 6(5): 405-416
  146. 146.
    Wu Y W and Ai X Y. 2008. Face detection in color images using AdaBoost algorithm based on skin color information//Proceedings of the First International Workshop on Knowledge Discovery and Data Mining. Adelaide, SA, Australia: IEEE: 339-342
  147. 147.
    Wu Z and Leahy R. 1993. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11): 1101-1113
  148. 148.
    Xiao X M, Cui H T, Yao M B, Fu Y G and Qi W Q. 2018. Auto rock detection via sparse-based background modeling for mars rover//Proceedings of 2018 IEEE Congress on Evolutionary Computation. Rio de Janeiro, Brazil: IEEE: 1-6
  149. 149.
    Xiao X M, Cui H T, Yao M B and Tian Y. 2017. Autonomous rock detection on mars through region contrast. Advances in Space Research, 60(3): 626-635
  150. 150.
    Xie T L, Jiang H K, Wang J L, Tian X L and Xu A A. 2015. A new recognition algorithm of the lunar mare area basing on the DEM contrast//Proceedings of the International Conference on Advanced Materials and Engineering Structural Technology. Qingdao: [s.n.]
  151. 151.
    Xiong J. 2014. Research on Technology of Lunar Crater Detecting Based on Image Processing. Nanchang: East China Jiaotong University
  152. 152.
    Xu C Y and Prince J L. 1998. Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3): 359-369
  153. 153.
    Xu Q, Wang D, Xing S and Lan C Z. 2016. Mapping and characterization techniques of asteroid topography. Journal of Deep Space Exploration, 3(4): 356-362
  154. 154.
    Yang J and Kang Z. 2019. A gradient-region constrained level set method for autonomous rock detection from mars rover image//Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Enschede, Netherlands: [s.
  155. 155.
    Yao M J and Chen J P. 2018. The central symmetry analysis of wrinkle ridges in lunar mare serenitatis. Earth, Moon, and Planets, 121(1-2): 45-58
  156. 156.
    Yue Z, Li W, Di K, Liu Z and Liu J. 2015. Global mapping and analysis of lunar wrinkle ridges. Journal of Geophysical Research: Planets, 120(5): 978-994
  157. 157.
    Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090
  158. 158.
    Zhang J, Zhao Y, Zhou F Q and Chi M. 2017. Visual saliency-based vehicle manufacturer recognition using autoencoder pre-training deep neural networks//Proceedings of 2017 IEEE International Conference on Imaging Systems and Techniques. Beijing: IEEE: 1-6
  159. 159.
    Zhou F Q, Song Y, Liu L and Zheng D T. 2018. Automated visual inspection of target parts for train safety based on deep learning. IET Intelligent Transport Systems, 12(6): 550-555
  160. 160.
    Zhou Z P, Cheng W M, Zhou C H, Wan C and Cao Y Y. 2011. Characteristic analysis of the lunar surface and automatically extracting of the lunar morphology based on CE-1. Chinese Science Bulletin, 56(1): 18-26
  161. 161.
    Zhu J Y, Park T, Isola P and Efros A A. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2242-2251
  162. 162.
    Zou X D, Li C L, Liu J J, Wang W R, Li H and Ping J S. 2014. The preliminary analysis of the 4179 Toutatis snapshots of the Chang'E-2 flyby. Icarus, 229: 348-354
  163. 163.
    Zuber M T, Smith D E, Cheng A F, Garvin J B, Aharonson O, Cole T D, Dunn P J, Guo Y P, Lemoine F G, Neumann G A, Rowlands D D and Torrence M H. 2000. The shape of 433 Eros from the NEAR-shoemaker laser rangefinder. Science, 289(5487): 2097-2101

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