中文核心期刊

中国科技核心期刊

中国科学引文数据库（CSCD）来源期刊

中国高校百佳科技期刊

中国宇航学会深空探测技术专业委员会会刊

高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于光学图像的撞击坑识别研究综述

丁萌,李海波,曹云峰,庄丽葵

downloadPDF
文章导航> 深空探测学报(中英文）> 2015> 2(3): 195-202
丁萌, 李海波, 曹云峰, 庄丽葵. 基于光学图像的撞击坑识别研究综述[J]. 深空探测学报(中英文）, 2015, 2(3): 195-202. doi: 10.15982/j.issn.2095-7777.2015.03.001
引用本文: 丁萌, 李海波, 曹云峰, 庄丽葵. 基于光学图像的撞击坑识别研究综述[J]. 深空探测学报(中英文）, 2015, 2(3): 195-202.doi:10.15982/j.issn.2095-7777.2015.03.001
shu
DING Meng, LI Haibo, CAO Yunfeng, ZHUANG Likui. Research Survey of Passive Image-Based Impact Crater Detection[J]. Journal of Deep Space Exploration, 2015, 2(3): 195-202. doi: 10.15982/j.issn.2095-7777.2015.03.001
Citation: DING Meng, LI Haibo, CAO Yunfeng, ZHUANG Likui. Research Survey of Passive Image-Based Impact Crater Detection[J].Journal of Deep Space Exploration, 2015, 2(3): 195-202.doi:10.15982/j.issn.2095-7777.2015.03.001
shu

基于光学图像的撞击坑识别研究综述

doi:10.15982/j.issn.2095-7777.2015.03.001
  • 1.

    南京航空航天大学 民航学院, 南京 210016

  • 2.

    南京航空航天大学 自动化学院, 南京 210016

  • 3.

    南京航空航天大学 航天学院, 南京 210016

基金项目:国家自然科学基金资助项目(61203170);航天创新基金,江苏省研究生培养创新工程(KYLX_0282)

Research Survey of Passive Image-Based Impact Crater Detection

  • 1.

    College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

  • 2.

    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

  • 3.

    College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

  • 摘要:当前,随着深空探测研究工作的需要,将信息科学的图像处理、模式识别技术应用到空间探测领域成为必然。基于光学图像的撞击坑自主检测技术就是将信息科学的图像处理技术应用到空间科学研究中的一个很好例证,近年来得到了各国学者的重视。本文针对这一领域的相关研究进行了介绍与分析。首先,对这一技术的研究意义从地质学、天体表面结构和特征数据库建设、探测器导航三个角度加以说明;其次,详细阐述了该技术的研究现状,简要介绍了其中一些经典算法,并将相关算法分为三类:全自主检测算法、半自主检测算法和组合检测算法;最后,提出了该技术研究所面临的难点和未来研究方向与应用空间,以及介绍了作者在这一方面的研究进展。
    Abstract:Currently more and more technologies of information science, such as image processing and pattern recognition, are used in the development of space science. The autonomous crater detection which gains many researchers'attention in recent years is one of the good examples in this field. This paper introduces the autonomous crater detection technology from passive images in details. Firstly, the applications of autonomous crater detection in three areas including geology or astronomy, space database establishment and spacecraft navigation and positioning are discussed. Secondly, the state of autonomous crater detection technology is introduced, esp. some typical algorithms are addressed. In order to explain these methods clearly, the related algorithms are classified into three kinds: Unsupervised Detection, Supervised Detection and Combination Detection. Finally, the difficulties, further work of autonomous crater detection and the author's work are addressed shortly.
  • [1] 张玥,李清毅,许晓霞.月球表面地形数学建模方法[J].航天器环境工程,2007,24(6):341-343. [Zhang Y, Li Q Y, Xu X X. The mathematical modeling approach of lunar surface terrain[J]. Spacecraft Environment Engineering, 2007,24(6):341-343.]
    [2] 邓剑.月球探测器软着陆过程中的障碍识别[D].吉林大学, 2007.[Deng J. Lunar obstacle detection in the process of lunar lander's landing[D]. Jilin University, 2007.]
    [3] 郭烈,王荣本,毛晓燕,等.基于机器视觉的环境感知坑检测方法研究[C]//中国宇航学会深空探测技术专业委员会第二届学术会议论文集.北京:CDSET. 2005,12(16):143-144. [Guo L, Wang R B, Mao X Y, et al.CDSET-CSA the second academic conference proceedings[C]//The 2th CDSET papers, Beijng: CDSET. 2005,12(16):143-144.]
    [4] Tanaka K L. The stratigraphy of Mars[J]. Journal of Geophysical Research, 1986,91(30):E139-E158.
    [5] Neukum G, Konig B, Arkani-Hamed J. A study of lunar impact crater size-distributions[J]. Earth, Moon, and Planets, 1975,12(2):201-229.
    [6] Soderblom L A, Condit C D, West R A, et al. Martian planetwide crater distributions: implications for geologic history and surface processes[J]. Icarus, 1974,22(3):239-263.
    [7] Cintala M J, Mouginis-Mark P J. Martian fresh crater depth: more evidence for subsurface volatiles[J]. Geophysical Research Letters, 1980(7):329-332.
    [8] Rodionova J F, Dekchtyareva K I, Khramchikhin A A, et al. Morphological catalogue of the craters of Mars[M]. Noordwijk, The Netherlands: ESA-ESTEC, 2000.
    [9] Barlow N G. Revision of the "catalog of large martian impact craters[C]//Sixth International Conference on Mars. [S.l.]:[s.n.], 2003.
    [10] Barlow N G. Crater size-distributions and a revised martian relative chronology[J]. Icarus, 1988,75(2):285-305.
    [11] Yang C, et al. The Mars exploration rovers descent image motion estimation system[J]. IEEE Intelligent Systems, 2004(3):13-21.
    [12] Cheng Y, Johnson A E, Matthies L H, et al. Optical landmark detection for spacecraft navigation[C]//in Proc of the 13th AAS/AIAA Space Flight Mechanics Meeting, Ponce, Puerto Rico. [S.l.]: AIAA,2003:1785-1803, AAS 03-224.
    [13] Cheng Y. Autonomous landmark based spacecraft navigation system[C]//AAS/AIAA Space Flight Mechanics Meeting. Ponce: AIAA, 2003.
    [14] NASA. Autonomous landing and hazard avoidance technology[EB/OL]. http://www-robotics.jpl.nasa.gov/tasks/showTask.cfm?FuseAction=showTask&TaskID=84&tdaID=999986.
    [15] Huertas A. Passive imaging based multi-cue hazard detection for spacecraft safe landing[C]//2006 IEEE Aerospace Conference. [S.l.]: IEEE, 2006.
    [16] Cheng Y, Johnson A E, et al. Passive imaging based hazard detection for spacecraft safe landing[C]//2000 IEEE Aerospace Conference. [S.l.]:IEEE, 2000.
    [17] Leroy B, Medioni G, Johnson A E, et al. Crater detection for autonomous landing on asteroids[J]. Image Vis. Comput., 2001,19( 11) :787-792.
    [18] Honda R, Iijima Y, Konishi O. Mining of topographic feature from heterogeneous imagery and its application to lunar craters[C]//In Progress in Discovery Science: Final Report of the Japanese Discovery Science Project. New York: Springer-Verlag, 2002.
    [19] Bandeira L, Saraiva J, Pina P. Enhancing impact crater rims to increase recognition rates, in Proc[C].//VISAPP-1st Int. Conf. Computer Vision Theory Applications. Portugal:[s.n.], 2006:407-412.
    [20] Bandeira L, Saraiva J, Pina P. Impact crater recognition on Mars based on a probability volume created by template matching[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2007,45(12):4008-4015.
    [21] Barata T, Ivo Alves E, Saraiva J, et al. Automatic recognition of impact craters on the surface of mars[C]//In Proc. ICIAR. Porto,Portugal:[s.n.], 2004:489-496.
    [22] Kim J R, Muller J P, Morley J G. Quantitative assessment of automated crater detection on Mars[C]// In Proc. 20th ISPRS Congr.. Istanbul, Turkey:[s.n.], 2004:816-821.
    [23] Kim J R., Muller J P, Gasselt S V, et al. Automated crater detection, a new tool for Mars cartography and chronology[J]. Photogramm. Eng. Remote Sens, 2005,71(10):1205-1217.
    [24] Bue B D, Stepinski T F. Machine detection of martian impact craters from digital topography data[J]. IEEE Trans. Geosci. Remote Sens, 2007,45(1):265-274.
    [25] Honda R, Azuma R. Crater extraction and classification system for lunar images[J]. Mem. Fac. Sci. Kochi Univ. (Inform. Sci.), 2000(21):13-22.
    [26] Jahn H. Crater detection by linear filters representing the Hough transform[C]//In Proc. SPIE—ISPRS Commission III Symp.: Spatial Information from Digital Photogrammetry Computer Vision. Munich:[s.n.], 1994(2357):427-431.
    [27] Vinogradova T, Burl M, Mjolness E. Training of a crater detection algorithm for Mars crater imagery[C]//In Proc. IEEE Aerosp. Conf., Big Sky, MT:[s.n.], 2002(7):3201-3211.
    [28] Matsumoto N, Asada N, Demura H. Automatic crater recognition on digital terrain model[C]//In Proc. Lunar Planetary Sci. XXXVI. Houston:[s.n.], 2005.
    [29] Homma K, Yamamoto H, Isobe T, et al. Massively parallel processing for crater recognition[C]//In Proc. Lunar Planetary Sci. XXVIII.Houston:[s.n.],1997.
    [30] Flores-Méndez A. Crater marking and classification using computer vision[C]//In Progress in Pattern Recognition, Speech and Image Analysis, vol. 2905, Lecture Notes in Computer Science. New York: Springer-Verlag, 2003:79-86.
    [31] Michael G.Coordinate registration by automated crater recognition[J]. Planetary Space Sci, 2003,51(9):563-568.
    [32] Bandeira L P C, Saraiva J, Pina P. Development of a methodology for automated crater detection on planetary images[M]. Pattern recognition and image analysis. Germany: Springer Berlin Heidelberg, 2007.
    [33] Magee M. Automated identification of martain craters using image processing[M]. LPSCXXXIV, 2003.
    [34] Saraiva. A structured approach to automated crater detection[M]. LPSCXXXVII,2006.
    [35] Brumby S, Plesko C, Asphaug E. Evolving automated feature extraction algorithms for planetary science[C]//Proc. ISPRS WG IV/9: Extraterrestrial Mapping Workshop-Advances Planetary Mapping. Houston:[s.n.], 2003.
    [36] Plesko C, Werner S, Brumby S E, et al. A statistical analysis of automated crater counts in MOC and HRSC data[C]//Proc. Lunar Planetary Sci. XXXVII. Houston:[s.n.], 2006.
    [37] Hough P V C. Method and means for recognizing complex patterns: U.S., 3 069 654[P]. 1962-12-18.
    [38] Olson C F. Improving the generalized Hough transform through imperfect grouping[J]. Image and Vision Computing, 1998,16(9):627-634.
    [39] Olson. Decomposition of the hough transform: curve detection with efficient erorpropagation[C]//Proceedings of the European conference on Computer Vision, [S.l.]:[s.n.],1996.
    [40] Xu L, Oja E, Kultaned P. A new curve detection method: Randomized Hough Transform(RHT)[J]. Pattern Recognition Letter, 1990,11(5):331-338.
    [41] Honda. Data mining system for planetary images-crater detection and categorization[J].http://citeseer.ist.psu.edu/436870.html.
    [42] Johnson A E, Klump A. LIDAR-base hazard avoidance for safe landing on Mars[C]//AAS/AIAA Space Flight Mechanics Meeting. Santa Barbara: AIAA, 2001.
    [43] Johnson A E. Surface landmark selection and matching in natural terrain[C]//Proc. IEEE Computer Vision and Pattern Recognition. [S.l.]: IEEE, 2000.
    [44] 杨忠根,马彦.使用广义正交概念的 K—RANSAC椭圆提取[J].自动化学报,2002,28(4):520-526.[Yang Z G, Ma Y. A new method for ellipse detection using K-RANSAC based on generalized orthogonality principle[J]. Acta Automatica Sinica, 2002,28(4):520-526.]
    [45] Nagase T, Agui T, Nagahashi H. Pattern matching of binary shapes using a genetic algorithm[J]. Transaction of IEICE, 1993,J76-D-II(3):557-565.
    [46] Watanabe T, Shibata T. Detection of broken ellipse by the Hough Transforms and multi-resolutionalImages[J]. Transaction of IEICE, 1990, J83-D-2(2):159-166.
    [47] Kohonen T. Self-organizing maps, 2nd eds[M]. Germany:Springer,1997.
    [48] Burl M C, Stough T, Colwell W, et al. Automated detection of craters and other geological features[C]//In Proc. Int. Symp. Artif. Intell., Robot. andAutom. Space, Montreal, QC. Canada:[s.n.], 2001.
    [49] 陈建清, 朱圣英, 崔祜涛, 等. 应用灰度特征的行星陨石坑自主检测方法与着陆导航研究[J]. 宇航学报, 2014, 35(8): 908-915.[Chen J Q, Zhu S Y, Cui H T, et al. Automated crater detection method using gray value features and Planet landing navigation research[J]. Journal of Astronautics, 2014,35(8):908-915.]
    [50] Vinogradova T, Burl M, Mjosness E. Training of a crater detection algorithm for Mars crater imagery[C]//In Proc. IEEE Aerosp. Conf. [S.l.]: IEEE, 2002.
    [51] Wetzler P G, Enke B, Merline W J, et al. Learning to detect small impact craters[C]//In Proc. 7th IEEEWACV/MOTION. [S.l]:[s.n.], 2005.
    [52] Smirnov A. Exploratory study of automated crater detection algorithm, dept. of computer science, university of colorado[R]. Internal Report, www. cs. colorado. edu/rossnd/fcdmf/CraterPaper. pdf, 2002.
    [53] 岳宗玉,刘建忠,吴淦国.应用面向对象分类方法对月球撞击坑进行自动识别[J].科学通报,2008,53(22):2809-2813. [Yue Z Y, Liu J Z, WU G G. Automatic identification to lunar impact crater using object-oriented sorting technique[J]. Science, 2008,53(22):2809-2813.]
    [54] Sawabe Y, Matsunaga T, Rokugawa S. Automated detection and classification of lunar craters using multiple approaches[J]. Adv. Space Res., 2006,37(1):21-27.
    [55] Magee M, Chapman C R, Dellenback S W, et al. Automated identification of Martian craters using image processing[C]//34th Lunar and Planetary Science Conference; Abstracts of Papers. [S.l.]:[s.n.], 2003.
    [56] Earl J, Chicarro A F, Koeberl C, et al. Automatic recognition of crater-like structures in terrestrial and planetary images[C]//In Proc. Lunar Planetary Sci. XXXVI. Houston:[s.n.], 2005.
    [57] Bandeira L, Saraiva J, Pina P. Development of a methodology for automated crater detection on planetary images[C]//In Proc.Iberian Conf. Pattern Recog. Image Anal. [S.l.]:[s.n.], 2007.
    [58] 张锋,邹永廖,郑永春,等.月表撞击坑自动识别与提取的新方法及其应用[J].地学前缘,2012,19(006):118-127.[Zhang F, Zou Y L, Zheng Y C, et al.A new automated approach to detecting and extracting lunar craters and its application[J]. Earth Science Frontiers, 2012,19(6):118-127.]
    [59] 罗中飞,康志忠,刘心怡.融合嫦娥一号CCD影像与DEM数据的月球撞击坑自动提取和识别[J].测绘学报,2014,43(9):924-930. [Luo Z F, Kang Z Z, Liu X Y. The automatic extraction and recognition of lunar impact craters fusing CCD[J]. Acta Geodaetica et Cartographica Sinica, 2014,43(9):924-930.]
    [60] Ding M, Cao Y F, Wu Q X. Autonomous craters detection from digital image[C]//The 3th International Conference On Innovation Computing, Intelligence and Contro. [S.l.]:[s.n.], 2008.
  • [1] 柳景兴, 王彬, 毛维杨, 熊新.深空探测器任务规划认知图谱及多属性约束冲突检测. 深空探测学报(中英文）, 2023, 10(1): 88-96.doi:10.15982/j.issn.2096-9287.2023.20220064
    [2] 邓玉, 郭华东, 刘广, 叶罕霖, 黄靖.月基对地观测图像模拟与应用研究. 深空探测学报(中英文）, 2022, 9(3): 269-277.doi:10.15982/j.issn.2096-9287.2022.20210073
    [3] 黄翔宇, 徐超, 胡荣海, 郭敏文.小行星动能撞击自主导航与制导方法研究. 深空探测学报(中英文）, 2022, 9(4): 438-446.doi:10.15982/j.issn.2096-9287.2022.20220054
    [4] 刘佳, 刘斌, 邸凯昌, 岳宗玉, 于天一, 王镓, 芶盛.“天问一号”着陆区地貌解译与定量分析. 深空探测学报(中英文）, 2022, 9(3): 329-337.doi:10.15982/j.issn.2096-9287.2022.20210123
    [5] 郑惠欣, 彭玉明, 王伟, 谢攀, 陆希, 李海洋, 陈晓.深空高速撞击的变系数末制导律设计方法. 深空探测学报(中英文）, 2021, 8(5): 511-518.doi:10.15982/j.issn.2096-9287.2021.20200095
    [6] 徐超, 黄翔宇, 李茂登.基于未知陆标图像测量的自主相对导航方法研究. 深空探测学报(中英文）, 2021, 8(2): 198-204.doi:10.15982/j.issn.2096-9287.2021.20200086
    [7] 孟庆宇, 付中梁, 董吉洪, 王栋.火星探测高分辨率可见光相机光学系统设计. 深空探测学报(中英文）, 2018, 5(5): 458-464.doi:10.15982/j.issn.2095-7777.2018.05.008
    [8] 平劲松, 黄倩, 鄢建国, 孟治国, 王明远.藏匿在冯·卡门坑南部下方的月球质量瘤. 深空探测学报(中英文）, 2018, 5(1): 34-40.doi:10.15982/j.issn.2095-7777.2018.01.005
    [9] 胡硕, 孟治国, 朱蕴哲, 连懿, 李翠, GUSEVAlexander.基于“嫦娥2号”CELMS数据的冯·卡门撞击坑微波辐射特性. 深空探测学报(中英文）, 2018, 5(1): 20-26.doi:10.15982/j.issn.2095-7777.2018.01.003
    [10] 张吉栋, 孟治国, 朱蕴哲, 曾昭发, 平劲松.基于LOLA数据的冯·卡门撞击坑太阳辐射研究. 深空探测学报(中英文）, 2018, 5(1): 12-19.doi:10.15982/j.issn.2095-7777.2018.01.002
    [11] 孟治国, 李翠, 平劲松, 黄倩, 蔡占川, AlexanderGusev.月面冯·卡门撞击坑的着陆选址和科学探测目标浅析. 深空探测学报(中英文）, 2018, 5(1): 3-11.doi:10.15982/j.issn.2095-7777.2018.01.001
    [12] 郑晨, 姚鸿泰.基于影像数据的冯·卡门撞击坑形貌分析. 深空探测学报(中英文）, 2018, 5(1): 50-56.doi:10.15982/j.issn.2095-7777.2018.01.007
    [13] 张迅与, 徐天奕, 李翠.冯·卡门撞击坑物质成分研究. 深空探测学报(中英文）, 2018, 5(1): 66-70.doi:10.15982/j.issn.2095-7777.2018.01.009
    [14] 张韵, 刘岩, 李俊峰.小行星防御动能撞击效果评估. 深空探测学报(中英文）, 2017, 4(1): 51-57.doi:10.15982/j.issn.2095-7777.2017.01.008
    [15] 程宇峰, 润一, 王密.深空探测光学导航敏感器在轨几何定标方法. 深空探测学报(中英文）, 2016, 3(3): 228-236.doi:10.15982/j.issn.2095-7777.2016.03.006
    [16] 席莎, 邵巍.一种基于多尺度边缘提取的陨石坑检测算法. 深空探测学报(中英文）, 2016, 3(4): 384-388.doi:10.15982/j.issn.2095-7777.2016.04.011
    [17] 徐超, 王大轶, 黄翔宇.基于陆标图像的火星精确着陆自主导航方法研究. 深空探测学报(中英文）, 2016, 3(2): 150-155.doi:10.15982/j.issn.2095-7777.2016.02.009
    [18] 徐青, 王栋, 邢帅, 蓝朝桢.小行星形貌测绘与表征技术. 深空探测学报(中英文）, 2016, 3(4): 356-362.doi:10.15982/j.issn.2095-7777.2016.04.007
    [19] 王栋, 徐青, 邢帅, 刘衷瑞.小行星形貌特征的分析与描述. 深空探测学报(中英文）, 2015, 2(4): 358-364.doi:10.15982/j.issn.2095-7777.2015.04.010
    [20] 张腾宇, 金双根, 崔祜涛.利用数字高程模型自动检测火星表面陨石坑. 深空探测学报(中英文）, 2014, 1(2): 123-127.
  • 加载中
WeChat 点击查看大图
计量
  • 文章访问数:3035
  • HTML全文浏览量:52
  • PDF下载量:2370
  • 被引次数:0
出版历程
  • 收稿日期:2014-12-15
  • 修回日期:2015-04-14

基于光学图像的撞击坑识别研究综述

doi:10.15982/j.issn.2095-7777.2015.03.001
  • 1. 南京航空航天大学 民航学院, 南京 210016
  • 2. 南京航空航天大学 自动化学院, 南京 210016
  • 3. 南京航空航天大学 航天学院, 南京 210016
    • 基金项目:国家自然科学基金资助项目(61203170);航天创新基金,江苏省研究生培养创新工程(KYLX_0282)
  • 收稿日期:2014-12-15
  • 修回日期:2015-04-14

摘要:当前,随着深空探测研究工作的需要,将信息科学的图像处理、模式识别技术应用到空间探测领域成为必然。基于光学图像的撞击坑自主检测技术就是将信息科学的图像处理技术应用到空间科学研究中的一个很好例证,近年来得到了各国学者的重视。本文针对这一领域的相关研究进行了介绍与分析。首先,对这一技术的研究意义从地质学、天体表面结构和特征数据库建设、探测器导航三个角度加以说明;其次,详细阐述了该技术的研究现状,简要介绍了其中一些经典算法,并将相关算法分为三类:全自主检测算法、半自主检测算法和组合检测算法;最后,提出了该技术研究所面临的难点和未来研究方向与应用空间,以及介绍了作者在这一方面的研究进展。

English Abstract

丁萌, 李海波, 曹云峰, 庄丽葵. 基于光学图像的撞击坑识别研究综述[J]. 深空探测学报(中英文）, 2015, 2(3): 195-202. doi: 10.15982/j.issn.2095-7777.2015.03.001
引用本文: 丁萌, 李海波, 曹云峰, 庄丽葵. 基于光学图像的撞击坑识别研究综述[J]. 深空探测学报(中英文）, 2015, 2(3): 195-202.doi:10.15982/j.issn.2095-7777.2015.03.001
shu
DING Meng, LI Haibo, CAO Yunfeng, ZHUANG Likui. Research Survey of Passive Image-Based Impact Crater Detection[J]. Journal of Deep Space Exploration, 2015, 2(3): 195-202. doi: 10.15982/j.issn.2095-7777.2015.03.001
Citation: DING Meng, LI Haibo, CAO Yunfeng, ZHUANG Likui. Research Survey of Passive Image-Based Impact Crater Detection[J].Journal of Deep Space Exploration, 2015, 2(3): 195-202.doi:10.15982/j.issn.2095-7777.2015.03.001
shu

    全文HTML

参考文献 (60)

目录

    /

      返回文章
      返回

        您是第976321位访问者

        版权所有: 《深空探测学报》（中英文）编辑部编辑部地址: 中关村南大街5号邮编: 100081电话: （010）81384396/81384397

        本系统由北京仁和汇智信息技术有限公司开发 百度统计

        Baidu
        map