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郑晨, 姚鸿泰. 基于影像数据的冯·卡门撞击坑形貌分析[J]. 深空探测学报(中英文), 2018, 5(1): 50-56. DOI: 10.15982/j.issn.2095-7777.2018.01.007
引用本文: 郑晨, 姚鸿泰. 基于影像数据的冯·卡门撞击坑形貌分析[J]. 深空探测学报(中英文), 2018, 5(1): 50-56. DOI: 10.15982/j.issn.2095-7777.2018.01.007
ZHENG Chen, YAO Hongtai. Topography Analysis of the Von Kármán Crater Based on the Observed Images[J]. Journal of Deep Space Exploration, 2018, 5(1): 50-56. DOI: 10.15982/j.issn.2095-7777.2018.01.007
Citation: ZHENG Chen, YAO Hongtai. Topography Analysis of the Von Kármán Crater Based on the Observed Images[J]. Journal of Deep Space Exploration, 2018, 5(1): 50-56. DOI: 10.15982/j.issn.2095-7777.2018.01.007

基于影像数据的冯·卡门撞击坑形貌分析

Topography Analysis of the Von Kármán Crater Based on the Observed Images

  • 摘要: 月球背面南极–艾肯(South Pole-Aitken,SPA)盆地内的冯·卡门(Von Kármán)撞击坑是“嫦娥4号”初步选定的着陆区,该区域的地形地貌分析是任务设计的一个重要环节。根据LOLA (Lunar Orbiter Laser Altimeter)高程数据和LROC(Lunar Reconnaissance Orbiter Camera)影像数据,建立了马尔科夫随机场模型,从聚类的角度对该区域的地形地貌进行了表示和分析。具体而言,利用模型中的似然函数对呈近似正态分布的观测数据进行了建模,模型的标记随机场则刻画了不同地物形貌间的空间关系,并通过概率推断求解出最终的聚类结果。实验结果表明,从聚类角度进行数据表示,可以更好地展示出冯·卡门撞击坑内低对比度区域的地形地貌;而通过设置不同的聚类数目,还有助于分析冯·卡门撞击坑内整体的地形分布与局部的典型地貌。

     

    Abstract: The Von Kármán crater, located in the South Pole-Aitken basin on the lunar far-side, is initially selected as the landing area for the Chang’e-4 mission, and its topography analysis is an important part of the mission design. In this paper, a Markov random field model(MRF)is employed to analyze the elevation data of the Lunar Orbiter Laser Altimeter(LOLA)and the image data of the Lunar Reconnaissance Orbiter Camera(LROC), which is developed to capture the topography of Von Kármán crater from the perspective of clustering. The likelihood function of the MRF model is used to describe the observed data with approximate normal distribution, and the label random field is designed to model the spatial relationship between data, and the probability inference is finally employed to obtain the clustering result. Experimental results show that clustering can effectively illustrate the topography at some low-contrast regions, and they can also assist the overall and local topography analysis by setting different clustering numbers.

     

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