Automatic Extraction of Lunar Craters Based on Neighborhood Analysis of Linear Window Using DEM

doi:10.13474/j.cnki.11-2246.2018.0171

Abstract

Abstract: Craters of the moon is the most intuitive,typical geological units,so the extraction and analysis of lunar craters is of great significance to reveal the geological evolution process.This study designs neighborhood analysis of linear window for automatic extraction of lunar craters by using the 100 meters resolution DEM data derived from the Lunar reconnaissance orbiter (LRO) of America.The results of experiment by two different areas in lunar mare and lunar land show that the best extraction window of lunar mare is 1×7 and 7×1,the best extraction threshold is 4.5. The best extraction window of lunar land is 1×9 and 9×1,the best extraction threshold is 6.This method can reach 78% on the extraction rate of the craters compared to results of visual recognition.The results of evaluation factors show that the extraction accuracy of this method is more than 77%,and the extraction quality is above 70%.

Key words: DEM, lunar crater, linear window, neighborhood analysis, automatic extraction

CLC Number:

P237

TU Jie, ZHOU Yi, ZHAO Hao, YAN Long, WU Jinyong. Automatic Extraction of Lunar Craters Based on Neighborhood Analysis of Linear Window Using DEM[J]. 测绘通报, 2018, 0(6): 27-33.

References

[1] LUO L,WANG X,GUO H,et al.Automatic Detection of Lunar Craters Based on CE-1 DEM Data[J].Journal of Remote Sensing,2014(1):105-116.
[2] 欧阳自远.月球科学概论[M].北京:中国宇航出版社,2005:58-64.
[3] HUANG Q,PING J,SU X,et al.New Features of the Moon Revealed and Identified by CLTM-s01[J].Science in China:Physics,Mechanics and Astronomy,2009,52(12):1815-1823.
[4] 王娇,程维明,周成虎.全月球撞击坑识别、分类及空间分布[J].地理科学进展,2015,34(3):330-339.
[5] LEROY B,MEDIONI G,JOHNSON E,et al.Crater Detection for Autonomous Landing on Asteroids[J].Image & Vision Computing,2001,19(11):787-792.
[6] 李超,王心源,骆磊,等.基于Apollo图像的月表撞击坑自动提取[J].国土资源遥感,2012,24(4):71-75.
[7] BARATA T,ALVES E I,SARAIVA J,et al.Automatic Recognition of Impact Craters on the Surface of Mars[J].Lecture Notes in Computer Science,2004,3212(3212):489-496.
[8] BANDEIRA L,WEI D,STEPINSKI T F.Detection of Sub-kilometer Craters in High Resolution Planetary Images Using Shape and Texture Features[J].Advances in Space Research,2012,49(1):64-74.
[9] 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.
[10] 陈伟涛,闫柏琨,张志.基于嫦娥一号CCD数据空间特征的特定目标识别[J].国土资源遥感,2009,21(4):40-44.
[11] BARNES A H.An Object-oriented Classification of Impact Craters Using Lunar Reconnaissance Orbiter Data[D].San Francisco,California:San Francisco State University,2016.
[12] DING M,CAO Y,WU Q.Method of Passive Image Based Crater Autonomous Detection[J].Chinese Journal of Aeronautics,2009,22(3):301-306.
[13] HONDA R,AZUMA R.Crater Extraction and Classification System for Lunar Images[R].Kochi:Kochi University,2000:13-22.
[14] 魏士俨,张建利,彭松,等.虹湾地区月面撞击坑自动提取[J].计算机仿真,2013,30(8):74-77.
[15] BANDEIRA L,SARAIVA J,PINA P.Impact Crater Recognition on Mars Based on a Probability Volume Created by Template Matching[J].IEEE Transactions on Geoscience & Remote Sensing,2007,45(12):4008-4015.
[16] BANDEIRA L P C,SARAIVA J,PINA P.Development of a Methodology for Automated Crater Detection on Planetary Images[J].Springer Berlin Heidelberg,2007,4447(4):193-200.
[17] WANG Y,YANG G,GUO L.A Novel Sparse Boosting Method for Crater Detection in the High Resolution Planetary Image[J].Advances Space Research,2015,56(5):982-991.
[18] XIN X,DI K C,WANG Y X,et al.Automated Detection of New Impact Sites on Martian Surface from HiRISE Images[J].Advancesm Space Research,2017,60(7):1557-1569.
[19] 丁萌,李海波,曹云峰,等.基于光学图像的撞击坑识别研究综述[J].深空探测学报,2015,2(3):195-202.
[20] MICHAEL G G.Coordinate Registration by Automated Crater Recognition[J].Planetary & Space Science,2003,51(9/10):563-568.
[21] BUE B D,STEPINSKI T F.Machine Detection of Martian Impact Craters from Digital Topography Data[J].IEEE Transactions on Geoscience & Remote Sensing,2006,45(1):265-274.
[22] HARADA N,HAYASHI T,HIRATA N,et al.Recognition Algorithm for Topographic Features[C]//17th IEEE International Conference on Computer and Information Technology(CIT 2007).[S.l.]:CIT,2007:685-689.
[23] DI K,LI W,YUE Z,et al.A Machine Learning Approach to Crater Detection from Topographic Data[J].Advances in Space Research,2014,54(11):2419-2429.
[24] COHEN J P.Automated Crater Detection Using Machine Learning[D].Boston:University of Massachusetts Boston,2016.
[25] 刘宇轩,刘建军,牟伶俐,等.撞击坑识别方法综述[J].天文研究与技术,2012,9(2):203-212.
[26] 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(6):118-127.
[27] 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.
[28] KANG Z,LUO Z,HU T.Automatic Extraction and Identification of Lunar Impact Craters Based on Optical Data and DEMs Acquired by the Chang'E Satellites[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2016,8(10):4751-4761.
[29] SALAMUNIC'CAR G,LONČARIC' S,PINA P.Integrated Method for Crater Detection from Topography and Optical Images and the New PH9224GT Catalogue of Phobos Impact Craters[J].Advances in Space Research,2014,53(12):1798-1809.
[30] 周增坡,程维明,周成虎,等.基于"嫦娥一号"的月表形貌特征分析与自动提取[J].科学通报,2011,58(1):18-26.
[31] 贺力.基于"DEM"的月球撞击坑判识方法研究[D].南京:南京师范大学,2012.
[32] 王琛智,汤国安,袁赛,等.基于DEM纹理特征的月貌自动识别方法探究[J].地球信息科学学报,2015,17(1):45-53.
[33] 王梁,丁孝忠,韩坤英,等.月球数字地质图的编制与研究[J].中国地质,2015(1):331-341.
[34] 周毅,汤国安,张婷,等.基于格网DEM线状分析窗口的地形特征线快速提取方法[J].测绘通报,2007(10):67-69.
[35] 陶兴龙.数学形态学和分形理论在图像去噪和边缘提取中的应用[D].南京:南京信息工程大学,2013.
[36] 袁悦峰,朱培民,赵娜,等.基于数学形态学的月海圆形撞击坑自动识别方法[J].中国科学(物理学力学天文学),2013,43(3):324-332.
[37] SHUFELT J.Performance Evaluation and Analysis of Monocular Building Extraction from Aerial Imagery[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,1999,21(4):311-326.
[38] ZUO W,ZHANG Z,LI C,et al.Contour-based Automatic Crater Recognition Using Digital Elevation Models from Chang'E Missions[J].Computers & Geosciences,2016,97(C):79-88.