浏览全部资源
扫码关注微信
纸质出版日期： 2021-01-07 ，
扫 描 看 全 文
邸凯昌，叶乐佳，王润之，王晔昕.2021.行星遥感影像目标识别与分类进展.遥感学报，25（1）： 365-380
Di K C，Ye L J，Wang R Z and Wang Y X. 2021. Advances in planetary target detection and classification using remote sensing data. National Remote Sensing Bulletin， 25（1）：365-380
从行星遥感海量数据中对地形地貌特征进行识别和分类，是行星科学研究中的一项重要基础工作。本文综述了自实施月球和深空探测任务以来，国际、国内采用行星影像数据进行地形地貌识别与分类技术的研究进展。首先，从月球、火星以及其他行星探测任务3个方面，对相关的探测任务和获取的影像数据进行简介。然后，在介绍通用目标识别与分类方法研究进展的基础上，分别详细阐述了月球、火星、其他行星影像数据的目标识别与分类研究进展，具体包括：（1）在月球影像目标识别与分类研究进展中，从月球表面环形构造识别，线性构造识别，以及地形分类几个方面展开详述；（2）在火星影像目标识别与分类研究进展中，从构造地貌，风成地貌，水成地貌，其他地貌的识别与地形分类几个方面的研究进展进行详述；（3）其他行星影像目标识别与分类研究进展中，从太阳系的其他类地行星和小行星的影像目标识别与地形分类研究进展进行阐述，其中针对小行星近距离飞越探测、绕飞探测、附着就位探测和表面采样返回等探测方式，分别介绍了对其影像数据的目标识别与地形分类的研究进展。最后，对行星遥感影像目标识别和分类技术的未来发展方向进行了展望和探讨。
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.
行星探测遥感数据目标识别地形分类机器学习
planetary explorationremote sensing datatarget recognitionterrain classificationmachine learning
Adams R and Bischof L. 1994. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6): 641-647 [DOI: 10.1109/34.295913http://dx.doi.org/10.1109/34.295913]
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 [DOI: 10.1016/j.asr.2011.08.021http://dx.doi.org/10.1016/j.asr.2011.08.021]
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 [DOI: 10.1109/LGRS.2010.2098390http://dx.doi.org/10.1109/LGRS.2010.2098390]
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 [DOI: 10.1002/esp.3323http://dx.doi.org/10.1002/esp.3323]
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 [DOI: 10.1144/SP401.5http://dx.doi.org/10.1144/SP401.5]
Barlow N G. 1988. Crater size-frequency distributions and a revised Martian relative chronology. Icarus, 75(2): 285-305 [DOI: 10.1016/0019-1035(88)90006-1http://dx.doi.org/10.1016/0019-1035(88)90006-1]
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.]
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 [DOI: 10.1016/j.icarus.2012.02.029http://dx.doi.org/10.1016/j.icarus.2012.02.029]
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 [DOI: 10.2458/azu_uapress_9780816532131-ch023http://dx.doi.org/10.2458/azu_uapress_9780816532131-ch023]
Bergonio J R, Rottas K M and Schorghofer N. 2013. Properties of martian slope streak populations. Icarus, 225(1): 194-199 [DOI: 10.1016/j.icarus.2013.03.023http://dx.doi.org/10.1016/j.icarus.2013.03.023]
Bue B D and Stepinski T F. 2006. Automated classification of landforms on Mars. Computers and Geosciences, 32(5): 604-614 [DOI: 10.1016/j.cageo.2005.09.004http://dx.doi.org/10.1016/j.cageo.2005.09.004]
Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6): 679-698[DOI: 10.1109/TPAMI.1986.4767851http://dx.doi.org/10.1109/TPAMI.1986.4767851]
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 [DOI: 10.1016/j.knosys.2018.10.011http://dx.doi.org/10.1016/j.knosys.2018.10.011]
Castelvecchi D. 2018. Daring Japanese mission reaches unexplored asteroid Ryugu. Nature, 558(7711): 495-496 [DOI: 10.1038/d41586-018-05544-9http://dx.doi.org/10.1038/d41586-018-05544-9]
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 [DOI: 10.2991/mmme-16.2016.14http://dx.doi.org/10.2991/mmme-16.2016.14]
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 [DOI: 10.2991/mmebc-16.2016.224http://dx.doi.org/10.2991/mmebc-16.2016.224]
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 [DOI: 10.1109/TPAMI.2017.2699184http://dx.doi.org/10.1109/TPAMI.2017.2699184]
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 [DOI: 10.1109/TGRS.2018.2806371http://dx.doi.org/10.1109/TGRS.2018.2806371]
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 [DOI: 10.1007/978-3-642-17277-9_1http://dx.doi.org/10.1007/978-3-642-17277-9_1]
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 [DOI: 10.1029/2007GL031111http://dx.doi.org/10.1029/2007GL031111]
Cohen J P. 2016. Automated Crater Detection Using Machine Learning. Boston: University of Massachusetts Boston
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 [DOI: 10.1016/j.asr.2013.05.010http://dx.doi.org/10.1016/j.asr.2013.05.010]
Coleman G B and Andrews H C. 1979. Image segmentation by clustering. Proceedings of the IEEE, 67(5): 773-785 [DOI: 10.1109/PROC.1979.11327http://dx.doi.org/10.1109/PROC.1979.11327]
Cortes C and Vapnik V. 1995. Support-vector networks. Machine Learning, 20(3): 273-297 [DOI: 10.1007/BF00994018http://dx.doi.org/10.1007/BF00994018]
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.
n.]: B4.1-36-18
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 [DOI: 10.1109/CVPR.2005.177http://dx.doi.org/10.1109/CVPR.2005.177]
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 [DOI: 10.1109/JSTARS.2019.2918302http://dx.doi.org/10.1109/JSTARS.2019.2918302]
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 [DOI: 10.1016/j.asr.2019.07.017http://dx.doi.org/10.1016/j.asr.2019.07.017]
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 [DOI: 10.1016/j.asr.2014.08.018http://dx.doi.org/10.1016/j.asr.2014.08.018]
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
邸凯昌, 刘斌, 刘召芹. 2018. 火星遥感制图技术回顾与展望. 航天器工程, 27(1): 10-24 [DOI: 10.3969/j.issn.1673-8748.2018.01.002http://dx.doi.org/10.3969/j.issn.1673-8748.2018.01.002]
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
邸凯昌, 刘斌, 刘召芹, 邹永廖. 2016. 月球遥感制图回顾与展望. 遥感学报, 20(5): 1230-1242 [DOI: 10.11834/jrs.20166158http://dx.doi.org/10.11834/jrs.20166158]
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
邸凯昌, 刘召芹, 万文辉, 彭嫚. 2015. 月球和火星遥感制图与探测车导航定位. 北京: 科学出版社
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 [DOI: 10.1007/s12583-013-0316-3http://dx.doi.org/10.1007/s12583-013-0316-3]
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
杜培军, 夏俊士, 薛朝辉, 谭琨, 苏红军, 鲍蕊. 2016. 高光谱遥感影像分类研究进展. 遥感学报, 20(2): 236-256 [DOI: 10.11834/jrs.20165022http://dx.doi.org/10.11834/jrs.20165022]
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 [DOI: 10.1109/CVPR.2007.383257http://dx.doi.org/10.1109/CVPR.2007.383257]
Fang T, Huo H and Ma H P. 2016. Intelligent Interpretation of High-Resolution Remote Sensing Images. Beijing: Science Press
方涛, 霍宏, 马贺平. 2016. 高分辨率遥感影像智能解译. 北京: 科学出版社
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 [DOI: 10.1109/TGRS.2004.827257http://dx.doi.org/10.1109/TGRS.2004.827257]
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 [DOI: 10.1016/S0034-4257(97)00049-7http://dx.doi.org/10.1016/S0034-4257(97)00049-7]
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 [DOI: 10.1126/science.1125841http://dx.doi.org/10.1126/science.1125841]
Fukushima K. 1988. Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks, 1(2): 119-130 [DOI: 10.1016/0893-6080(88)90014-7http://dx.doi.org/10.1016/0893-6080(88)90014-7]
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 [DOI: 10.1109/AERO.2015.7118883http://dx.doi.org/10.1109/AERO.2015.7118883]
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 [DOI: 10.1016/j.robot.2013.11.003http://dx.doi.org/10.1016/j.robot.2013.11.003]
Glaude Q. 2017. CraterNet: A Fully Convolutional Neural Network for Lunar Crater Detection Based on Remotely Sensed Data. Belgium: University of Liège
Goodfellow I, Bengio Y and Courville A. 2016. Deep learning. Cambridge MA: MIT Press
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 [DOI: 10.1016/j.icarus.2017.11.028http://dx.doi.org/10.1016/j.icarus.2017.11.028]
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 [DOI: 10.1109/83.730380http://dx.doi.org/10.1109/83.730380]
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 [DOI: 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90]
Hinton G E, Osindero S and Teh Y W. 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18(7): 1527-1554 [DOI: 10.1162/neco.2006.18.7.1527http://dx.doi.org/10.1162/neco.2006.18.7.1527]
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 [DOI: 10.1038/srep03411http://dx.doi.org/10.1038/srep03411]
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 [DOI: 10.1109/76.736726http://dx.doi.org/10.1109/76.736726]
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 [DOI: 10.1109/CVPR.2017.632http://dx.doi.org/10.1109/CVPR.2017.632]
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 [DOI: 10.1109/34.730558http://dx.doi.org/10.1109/34.730558]
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 [DOI: 10.1088/1674-4527/15/9/016http://dx.doi.org/10.1088/1674-4527/15/9/016]
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 [DOI: 10.1016/j.pss.2014.04.021http://dx.doi.org/10.1016/j.pss.2014.04.021]
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 [DOI: 10.14358/PERS.71.10.1205http://dx.doi.org/10.14358/PERS.71.10.1205]
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.
n.]: 1679
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 [DOI: 10.1029/98JE00360http://dx.doi.org/10.1029/98JE00360]
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. [DOI: 10.1145/3065386http://dx.doi.org/10.1145/3065386]
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 [DOI: 10.1029/2018JE005545http://dx.doi.org/10.1029/2018JE005545]
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 [DOI: 10.1162/neco.1989.1.4.541http://dx.doi.org/10.1162/neco.1989.1.4.541]
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 [DOI: 10.1109/5.726791http://dx.doi.org/10.1109/5.726791]
Lee C. 2019. Automated crater detection on Mars using deep learning. Planetary and Space Science, 170: 16-28 [DOI: 10.1016/j.pss.2019.03.008http://dx.doi.org/10.1016/j.pss.2019.03.008]
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 [DOI: 10.1016/j.jsg.2018.01.003http://dx.doi.org/10.1016/j.jsg.2018.01.003]
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
李春来, 刘建军, 严韦, 封剑青, 任鑫, 刘斌. 2019. 小行星探测科学目标进展与展望. 深空探测学报, 6(5): 424-436 [DOI: 10.15982/j.issn.2095-7777.2019.05.003http://dx.doi.org/10.15982/j.issn.2095-7777.2019.05.003]
Li G Q, Geng Y H and Xiao X M. 2018b. Multi-scale rock detection on Mars. Science China Information Sciences, 61(10): 102301 [DOI: 10.1007/s11432-017-9277-xhttp://dx.doi.org/10.1007/s11432-017-9277-x]
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
李卉, 钟成. 2013. 基于激光测高数据的月表撞击坑自动检测方法. 地球科学——中国地质大学学报, 38(S1): 161-166
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
李婧, 陈建平, 王楠, 何姝珺. 2014. 月表线性构造自动提取新方法研究: 以澄海地区月岭为例. 地学前缘, 21(6): 223-228 [DOI: 10.13745/j.esf.2014.06.022http://dx.doi.org/10.13745/j.esf.2014.06.022]
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 [DOI: 10.1109/CISP.2011.6100554http://dx.doi.org/10.1109/CISP.2011.6100554]
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 [DOI: 10.14358/PERS.81.12.913http://dx.doi.org/10.14358/PERS.81.12.913]
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
黎战凯, 常伊人, 陈佳恒, 田小林. 2017. 一种新型月球地形自动识别迭代算法. 宇航学报, 38(1): 72-79 [DOI: 10.3873/j.issn.1000-1328.2017.01.010http://dx.doi.org/10.3873/j.issn.1000-1328.2017.01.010]
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 [DOI: 10.2991/icmmct-16.2016.315http://dx.doi.org/10.2991/icmmct-16.2016.315]
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 [DOI: 10.1109/ICIP.2002.1038171http://dx.doi.org/10.1109/ICIP.2002.1038171]
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
刘安, 周东华, 陈茂银. 2016. 分块鲁棒主成分分析的撞击坑图像检测识别. 北京邮电大学学报, 39(1): 63-67 [DOI: 10.13190/j.jbupt.2016.01.011http://dx.doi.org/10.13190/j.jbupt.2016.01.011]
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
刘宇轩, 李春来, 刘建军. 2018. 一种基于等高线的小型撞击坑识别方法. 天文研究与技术, 15(4): 479-486 [DOI: 10.14005/j.cnki.issn 1672-7673.20180511.001http://dx.doi.org/10.14005/j.cnki.issn1672-7673.20180511.001]
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
刘宇轩, 刘建军, 牟伶俐, 李春来. 2012. 撞击坑识别方法综述. 天文研究与技术, 9(2): 203-212 [DOI: 10.14005/j.cnki.issn1672-7673.2012.02.013http://dx.doi.org/10.14005/j.cnki.issn1672-7673.2012.02.013]
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 [DOI: 10.1016/j.icarus.2018.04.006http://dx.doi.org/10.1016/j.icarus.2018.04.006]
Lo H Z. 2016. Deep Networks: Applications, Interpretability, and Optimization. Boston: University of Massachusetts Boston
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.]
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
娄艺蓝, 康志忠. 2018. 利用DEM平均值滤波法的月表线性构造信息提取. 测绘科学, 43(5): 155-160 [DOI: 10.16251/j.cnki.1009-2307.2018.05.027http://dx.doi.org/10.16251/j.cnki.1009-2307.2018.05.027]
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
罗中飞, 康志忠, 刘心怡. 2014. 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. 测绘学报, 43(9): 924-930 [DOI: 10.13485/j.cnki.11-2089.2014.0137http://dx.doi.org/10.13485/j.cnki.11-2089.2014.0137]
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 [DOI: 10.1109/ICIP.2015.7351201http://dx.doi.org/10.1109/ICIP.2015.7351201]
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 [DOI: 10.1109/34.368173http://dx.doi.org/10.1109/34.368173]
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 [DOI: 10.1016/B978-0-12-546090-3.50016-4http://dx.doi.org/10.1016/B978-0-12-546090-3.50016-4]
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 [DOI: 10.1109/LGRS.2008.2006004http://dx.doi.org/10.1109/LGRS.2008.2006004]
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 [DOI: 10.1016/j.cja.2013.02.016http://dx.doi.org/10.1016/j.cja.2013.02.016]
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 [DOI: 10.1016/j.cageo.2014.09.005http://dx.doi.org/10.1016/j.cageo.2014.09.005]
Molloy I and Stepinski T F. 2007. Automatic mapping of valley networks on Mars. Computers and Geosciences, 33(6): 728-738 [DOI: 10.1016/j.cageo.2006.09.009http://dx.doi.org/10.1016/j.cageo.2006.09.009]
Nogrady B. 2018. Japanese rover lands on ancient asteroid for 16-hour mission. Nature [DOI: 10.1038/d41586-018-06928-7http://dx.doi.org/10.1038/d41586-018-06928-7]
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 [DOI: 10.1109/AERO.2015.7119022http://dx.doi.org/10.1109/AERO.2015.7119022]
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 [DOI: 10.2514/6.2018-5419http://dx.doi.org/10.2514/6.2018-5419]
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 [DOI: 10.1109/AERO.2016.7500597http://dx.doi.org/10.1109/AERO.2016.7500597]
Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI: 10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076]
Ouyang Z Y. 2005. Introduction to Lunar science. Beijing: China Astronautic Publishing House
欧阳自远. 2005. 月球科学概论. 北京: 中国宇航出版社
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 [DOI: 10.1016/j.cageo.2016.12.015http://dx.doi.org/10.1016/j.cageo.2016.12.015]
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 [DOI: 10.1109/ICCV.1998.710772http://dx.doi.org/10.1109/ICCV.1998.710772]
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.
n.]: 1431-1435 [DOI: 10.5194/isprs-archives-XLII-2-W13-1431-2019http://dx.doi.org/10.5194/isprs-archives-XLII-2-W13-1431-2019]
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.
n.]: 2027
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 [DOI: 10.1016/S0094-5765(00)00087-4http://dx.doi.org/10.1016/S0094-5765(00)00087-4]
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 [DOI:10.1029/2018JE005592http://dx.doi.org/10.1029/2018JE005592]
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 [DOI: 10.1029/2011JE003966http://dx.doi.org/10.1029/2011JE003966]
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 [DOI: 10.1029/2011JE003967http://dx.doi.org/10.1029/2011JE003967]
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 [DOI: 10.2514/6.2016-5539http://dx.doi.org/10.2514/6.2016-5539]
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 [DOI: 10.1016/j.pss.2011.09.003http://dx.doi.org/10.1016/j.pss.2011.09.003]
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 [DOI: 10.1016/j.pss.2010.11.003http://dx.doi.org/10.1016/j.pss.2010.11.003]
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.]
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 [DOI: 10.1029/92JE01246http://dx.doi.org/10.1029/92JE01246]
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.
n.]: 8047
Schorghofer N, Aharonson O, Gerstell M F and Tatsumi L. 2007. Three decades of slope streak activity on Mars. Icarus, 191(1): 132-140 [DOI: 10.1016/j.icarus.2007.04.026http://dx.doi.org/10.1016/j.icarus.2007.04.026]
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 [DOI: 10.1007/3-540-47979-1_24http://dx.doi.org/10.1007/3-540-47979-1_24]
Schwenk H and Bengio Y. 2000. Boosting neural networks. Neural Computation, 12(8): 1869-1887 [DOI: 10.1162/0899766003000 15178http://dx.doi.org/10.1162/089976600300015178]
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 [DOI: 10.1016/j.cviu.2012.12.002http://dx.doi.org/10.1016/j.cviu.2012.12.002]
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 [DOI: 10.1016/j.icarus.2018.06.022http://dx.doi.org/10.1016/j.icarus.2018.06.022]
Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
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 [DOI: 10.1016/j.pss.2012.06.003http://dx.doi.org/10.1016/j.pss.2012.06.003]
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 [DOI: 10.1109/LGRS.2009.2024333http://dx.doi.org/10.1109/LGRS.2009.2024333]
Stepinski T F and Collier M L. 2004. Extraction of Martian valley networks from digital topography. Journal of Geophysical Research: Planets, 109 (E11): E11005 [DOI: 10.1029/2004JE002269http://dx.doi.org/10.1029/2004JE002269]
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 [DOI: 10.4018/978-1-4666-1806-0.ch008http://dx.doi.org/10.4018/978-1-4666-1806-0.ch008]
Strom R G. 1964. Analysis of lunar lineaments, I: tectonic maps of the moon. Communications of the Lunar and Planetary Laboratory, 2: 205-216
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 [DOI: 10.1016/j.cviu.2017.03.007http://dx.doi.org/10.1016/j.cviu.2017.03.007]
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 [DOI: 10.1021/ci034160ghttp://dx.doi.org/10.1021/ci034160g]
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 [DOI: 10.1109/CVPR.2015.7298594http://dx.doi.org/10.1109/CVPR.2015.7298594]
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 [DOI: 10.5121/sipij.2011.2414http://dx.doi.org/10.5121/sipij.2011.2414]
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 [DOI: 10.1007/978-3-540-75690-3_18http://dx.doi.org/10.1007/978-3-540-75690-3_18]
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
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 [DOI: 10.1109/AERO.2007.352699http://dx.doi.org/10.1109/AERO.2007.352699]
Unser M. 1995. Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing, 4(11): 1549-1560 [DOI: 10.1109/83.469936http://dx.doi.org/10.1109/83.469936]
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 [DOI: 10.1016/j.pss.2009.03.009http://dx.doi.org/10.1016/j.pss.2009.03.009]
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 [DOI: 10.1016/j.geomorph.2015.08.021http://dx.doi.org/10.1016/j.geomorph.2015.08.021]
Veverka J, Belton M, Klaasen K and Chapman C. 1994. Galileo's encounter with 951 Gaspra: overview. Icarus, 107(1): 2-17 [DOI: 10.1006/icar.1994.1002http://dx.doi.org/10.1006/icar.1994.1002]
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 [DOI: 10.1109/AERO.2002.1035297http://dx.doi.org/10.1109/AERO.2002.1035297]
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 [DOI: 10.1145/2168752.2168763http://dx.doi.org/10.1145/2168752.2168763]
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
王琛智, 汤国安, 袁赛, 孙建伟, 刘凯. 2015. 基于DEM纹理特征的月貌自动识别方法探究. 地球信息科学学报, 17(1): 45-53 [DOI: 10.3724/SP.J.1047.2015.00045http://dx.doi.org/10.3724/SP.J.1047.2015.00045]
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
王栋, 邢帅, 徐青, 耿迅, 施群山. 2015a. 一种基于三维形貌的深空星体表面撞击坑自动提取方法. 测绘科学技术学报, 32(6): 619-625 [DOI: 10.3969/j.issn.1673-6338.2015.06.015http://dx.doi.org/10.3969/j.issn.1673-6338.2015.06.015]
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
王栋, 徐青, 邢帅, 刘衷瑞. 2015b. 小行星形貌特征的分析与描述. 深空探测学报, 2(4): 358-364 [DOI: 10.15982/j.issn.2095-7777.2015.04.010http://dx.doi.org/10.15982/j.issn.2095-7777.2015.04.010]
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
王贺, 陈劲松, 余晓敏. 2013. 面向对象分类特征优化选取方法及其应用. 遥感学报, 17(4): 816-829 [DOI: 10.11834/jrs.20132257http://dx.doi.org/10.11834/jrs.20132257]
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 [DOI: 10.3390/rs10071067http://dx.doi.org/10.3390/rs10071067]
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
王娇, 程维明, 周成虎, 赵敏. 2014. 全月球撞击坑形貌特征的识别与多指标表达. 地理研究, 33(7): 1251-1263 [DOI: 10.11821/dlyj201407006http://dx.doi.org/10.11821/dlyj201407006]
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 [DOI: 10.1109/ICCV.2009.5459207http://dx.doi.org/10.1109/ICCV.2009.5459207]
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 [DOI: 10.1016/j.asr.2015.05.014http://dx.doi.org/10.1016/j.asr.2015.05.014]
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 [DOI: 10.1109/TGRS.2019.2902198http://dx.doi.org/10.1109/TGRS.2019.2902198]
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 [DOI: 10.1016/j.isprsjprs.2017.04.014http://dx.doi.org/10.1016/j.isprsjprs.2017.04.014]
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 [DOI: 10.1007/s11214-017-0377-1http://dx.doi.org/10.1007/s11214-017-0377-1]
Wilhelms D. 1990. Geologic mapping//Greeley R and Batson R, eds. Planetary Mapping. Cambridge, UK: Cambridge University Press
Wu W R, Dong G L and Li H T. 2013. Engineering and Technology of Deep Space TT and C System. Beijing: Science Press
吴伟仁, 董光亮, 李海涛. 2013. 深空测控通信系统工程与技术. 北京: 科学出版社
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
吴伟仁, 刘继忠, 唐玉华, 于登云, 于国斌, 张哲. 2019. 中国探月工程. 深空探测学报, 6(5): 405-416 [DOI: 10.15982/j.issn.2095-7777.2019.05.001http://dx.doi.org/10.15982/j.issn.2095-7777.2019.05.001]
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 [DOI: 10.1109/WKDD.2008.148http://dx.doi.org/10.1109/WKDD.2008.148]
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 [DOI: 10.1109/34.244673http://dx.doi.org/10.1109/34.244673]
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 [DOI: 10.1109/CEC.2018.8477665http://dx.doi.org/10.1109/CEC.2018.8477665]
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 [DOI: 10.1016/j.asr.2017.04.028http://dx.doi.org/10.1016/j.asr.2017.04.028]
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.]
Xiong J. 2014. Research on Technology of Lunar Crater Detecting Based on Image Processing. Nanchang: East China Jiaotong University
熊娟. 2014. 基于图像处理的月球撞击坑识别技术研究. 南昌: 华东交通大学
Xu C Y and Prince J L. 1998. Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3): 359-369 [DOI: 10.1109/83.661186http://dx.doi.org/10.1109/83.661186]
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
徐青, 王栋, 邢帅, 蓝朝桢. 2016. 小行星形貌测绘与表征技术. 深空探测学报, 3(4): 356-362 [DOI: 10.15982/j.issn.2095-7777.2016.04.007http://dx.doi.org/10.15982/j.issn.2095-7777.2016.04.007]
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.
n.]: 1479-1485 [DOI: 10.5194/isprs-archives-XLII-2-W13-1479-2019http://dx.doi.org/10.5194/isprs-archives-XLII-2-W13-1479-2019]
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 [DOI: 10.1007/s11038-018-9514-4http://dx.doi.org/10.1007/s11038-018-9514-4]
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 [DOI: 10.1002/2014JE004777http://dx.doi.org/10.1002/2014JE004777]
Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090
张兵. 2016. 高光谱图像处理与信息提取前沿. 遥感学报, 20(5): 1062-1090 [DOI: 10.11834/jrs.20166179http://dx.doi.org/10.11834/jrs.20166179]
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 [DOI: 10.1109/IST.2017.8261506http://dx.doi.org/10.1109/IST.2017.8261506]
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 [DOI: 10.1049/iet-its.2016.0338http://dx.doi.org/10.1049/iet-its.2016.0338]
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
周增坡, 程维明, 周成虎, 万丛, 曹玉尧. 2011. 基于“嫦娥一号”的月表形貌特征分析与自动提取. 科学通报, 56(1): 18-26 [DOI: 10.1360/972010-1375http://dx.doi.org/10.1360/972010-1375]
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 [DOI: 10.1109/ICCV.2017.244http://dx.doi.org/10.1109/ICCV.2017.244]
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 [DOI: 10.1016/j.icarus.2013.11.002http://dx.doi.org/10.1016/j.icarus.2013.11.002]
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 [DOI: 10.1126/science.289.5487.2097http://dx.doi.org/10.1126/science.289.5487.2097]
相关作者
相关机构
京公网安备11010802024621