Lunar Crater Detection Based on Terrain Analysis and Mathematical Morphology Methods Using Digital Elevation Models

Lunar impact craters are the most typical geomorphic feature on the moon and are of great importance in studies of lunar terrain features. This paper presents a crater detection algorithm (CDA) that is based on terrain analysis and mathematical morphology methods. The proposed CDA is applied to digital elevation models (DEMs) to identify the boundaries of impact craters. The topographic and morphological characteristics of impact craters are discussed, and detailed steps are presented to detect different types of craters, such as dispersal craters, connective craters, and con-craters. The DEM from the Lunar Reconnaissance Orbiter, which has a resolution of 100 m, is used to verify the proposed CDA. The results show that the boundaries of impact craters can be detected. The results enable increased understanding of surface processes through the characterization of crater morphometry and the use of crater size–frequency distributions to estimate the ages of planetary surfaces.

[1]  Jeffrey F. Bell,et al.  Lunar dark‐haloed impact craters: Origin and implications for Early Mare volcanism , 1984 .

[2]  Tomasz F. Stepinski,et al.  Automatic detection of sub-km craters in high resolution planetary images , 2009 .

[3]  Sven Loncaric,et al.  Integrated crater detection algorithm and systematic cataloging of phobos craters , 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

[4]  Qingxian Wu,et al.  Novel approach of crater detection by crater candidate region selection and matrix-pattern-oriented least squares support vector machine , 2013 .

[5]  Ji Wei,et al.  Automatic Detection of Lunar Elliptical Craters from Apollo Image , 2012 .

[6]  B. R. Hawke,et al.  Remote sensing studies of lunar dark-halo impact craters: Preliminary results and implications for early volcanism , 1982 .

[7]  Hui Lin,et al.  Crater Detection Using the Morphological Characteristics of Chang'E-1 Digital Elevation Models , 2013, IEEE Geoscience and Remote Sensing Letters.

[8]  Xueming Qian,et al.  Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Eric Mjolsness,et al.  Training of a crater detection algorithm for Mars crater imagery , 2002, Proceedings, IEEE Aerospace Conference.

[10]  S. Sanjeevi,et al.  Crater detection, classification and contextual information extraction in lunar images using a novel algorithm , 2013 .

[11]  Lawrence A. Taylor,et al.  Space weathering processes on airless bodies: Fe isotope fractionation in the lunar regolith , 2003 .

[12]  Erwan Mazarico,et al.  Global Distribution of Large Lunar Craters: Implications for Resurfacing and Impactor Populations , 2010, Science.

[13]  Matthew A. North,et al.  A Method for Implementing a Statistically Significant Number of Data Classes in the Jenks Algorithm , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[14]  Tang Guo-an,et al.  Comparison of Slope Classification Methods in Slope Mapping from DEMs , 2006 .

[15]  James W. Head,et al.  Radial thickness variation in impact crater ejecta - Implications for lunar basin deposits , 1973 .

[16]  Joseph Paul Cohen,et al.  Crater detection via genetic search methods to reduce image features , 2014 .

[17]  Larry H. Matthies,et al.  Crater detection for autonomous landing on asteroids , 2001, Image Vis. Comput..

[18]  Xuelong Li,et al.  Lazy Random Walks for Superpixel Segmentation , 2014, IEEE Transactions on Image Processing.

[19]  ZongYu Yue,et al.  Automated detection of lunar craters based on object-oriented approach , 2008 .

[20]  Lingli Mu,et al.  Global detection of large lunar craters based on the CE-1 digital elevation model , 2013, Frontiers of Earth Science.

[21]  Lei Luo,et al.  Automated detection of lunar craters based on Chang'E-1 CCD data , 2011, 2011 4th International Congress on Image and Signal Processing.

[22]  S. K. Jenson,et al.  Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .

[23]  Qiao Zhan-ming,et al.  Automatic Detection of Lunar Elliptical Craters from Apollo Image , 2012 .

[24]  K. Vinod Kumar,et al.  An object-based classification method for automatic detection of lunar impact craters from topographic data , 2016 .

[25]  Weiming Cheng,et al.  Automatic extraction of lunar impact craters from Chang’E-1 satellite photographs , 2012 .

[26]  Xuelong Li,et al.  Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features , 2018, IEEE Transactions on Image Processing.

[27]  Kaoru Hirota,et al.  A Hu moment invariant as a shape circularity measure , 2010, Pattern Recognit..

[28]  Danyang Liu,et al.  Boundary Detection of Dispersal Impact Craters Based on Morphological Characteristics Using Lunar Digital Elevation Model , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Michael C. Burl,et al.  Automated detection of craters and other geological features , 2001 .

[30]  Chao Li,et al.  A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[31]  Bo Wu,et al.  Integration of Chang'E-2 imagery and LRO laser altimeter data with a combined block adjustment for precision lunar topographic modeling , 2014 .

[32]  Shuichi Rokugawa,et al.  Automatic Crater Detection Algorithm for the Lunar Surface Using Multiple Approaches , 2005 .

[33]  Brian D. Bue,et al.  Machine Detection of Martian Impact Craters From Digital Topography Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[34]  William K. Hartmann,et al.  Cratering Records in the Inner Solar System in Relation to the Lunar Reference System , 2001 .

[35]  Nur Diyana Kamarudin,et al.  Craters detection on lunar , 2011, Proceeding of the 2011 IEEE International Conference on Space Science and Communication (IconSpace).

[36]  Xiangtao Zheng,et al.  Remote Sensing Scene Classification by Unsupervised Representation Learning , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Goran Salamuni,et al.  Method for Crater Detection From Martian Digital Topography Data Using Gradient Value/Orientation, Morphometry, Vote Analysis, Slip Tuning, and Calibration , 2010 .

[38]  Donald E. Gault,et al.  Saturation and Equilibrium Conditions for Impact Cratering on the Lunar Surface: Criteria and Implications , 1970 .

[39]  Xuelong Li,et al.  Robust Video Object Cosegmentation , 2015, IEEE Transactions on Image Processing.

[40]  Paolo Gamba,et al.  Automatic Extraction and Identification of Lunar Impact Craters Based on Optical Data and DEMs Acquired by the Chang’E Satellites , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  Paul L. Rosin.  Measuring shape: ellipticity, rectangularity, and triangularity , 2003, Machine Vision and Applications.

[42]  Hutao Cui,et al.  A new approach based on crater detection and matching for visual navigation in planetary landing , 2014 .

[43]  Hui Lin,et al.  Construction of a virtual lunar environment platform , 2013, Int. J. Digit. Earth.

[44]  Y. Wen,et al.  Shape characteristics-based extraction of lunar impact craters: using DEM from the Chang'E-1 satellite as a data source , 2013, Ann. GIS.

[45]  Steven P. Brumby,et al.  Applications of Machine Learning Techniques in Digital Processing of Images of the Martian Surface , 2002, SPIE Optics + Photonics.

[46]  Hui Lin,et al.  Sino-VirtualMoon: A 3D web platform using Chang’E-1 data for collaborative research , 2012 .

[47]  Zhou Chenghu,et al.  Multiple flow direction algorithm with flow partition scheme based on downslope gradient , 2006 .

[48]  Min Chen,et al.  Morphological Features-Based Descriptive Index System for Lunar Impact Craters , 2017, ISPRS Int. J. Geo Inf..

[49]  Wei Li,et al.  A machine learning approach to crater detection from topographic data , 2014 .

[50]  J. Muller,et al.  Automated crater detection, a new tool for Mars cartography and chronology , 2005 .

[51]  Xiaojun Chang,et al.  Revealing Event Saliency in Unconstrained Video Collection , 2017, IEEE Transactions on Image Processing.

[52]  Chenghu Zhou,et al.  A Chang'E-1 global catalog of lunar impact craters , 2015 .

[53]  Raymond E. Arvidson,et al.  Morphologic classification of Martian craters and some implications , 1974 .

[54]  Brian D. Bue,et al.  Automated classification of landforms on Mars , 2006, Comput. Geosci..

[55]  Keiken Ninomiya,et al.  Optical guidance for autonomous landing of spacecraft , 1999 .

[56]  Ricardo Vilalta,et al.  Digital topography models for Martian surfaces , 2005, IEEE Geoscience and Remote Sensing Letters.

[57]  Shuichi Rokugawa,et al.  Automated detection and classification of lunar craters using multiple approaches , 2006 .