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地球信息科学学报 >

2025 , Vol. 27 >Issue 4: 820 - 834

DOI: https://doi.org/10.12082/dqxxkx.2025.240474

专栏：“从地球到深空：遥感地理信息技术拓展与创新”

基于月貌基元的月球撞击坑识别与分类

  • 王娇 , 1, 3 ,
  • 李俊娇 1 ,
  • 芮琦瑶 1 ,
  • 程维明 , 2, *
展开
  • 1.中国地质大学（北京）信息工程学院，北京 100083
  • 2.中国科学院地理科学与资源研究所 地理信息科学与技术全国重点实验室，北京 100101
  • 3.河北省地理空间数字孪生与协同优化重点实验室，北京 100083
* 程维明（1973— ），男，甘肃天水人，研究员，主要从事数字地貌及月球形貌研究。E-mail:

王 娇（1990— ），女，宁夏固原人，副教授，主要从事月球形貌研究。E-mail:

Copy editor: 黄光玉 , 蒋树芳

收稿日期: 2024-08-27

  修回日期: 2024-12-18

  网络出版日期: 2025-03-25

基金资助

国家重点研发计划项目(2022YFF0711400)

国家重点研发计划项目(2019YFE0123300)

国家自然科学基金项目(41801361)

收起

Lunar Impact Crater Identification and Classification Based on Lunar Geomorphons

  • WANG Jiao , 1, 3 ,
  • LI Junjiao 1 ,
  • RUI Qiyao 1 ,
  • CHENG Weiming , 2, *
Expand
  • 1. School of Information and Engineering, China University of Geosciences (Beijing), Beijing 100083, China
  • 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 3. Hebei Key Laboratory of Geospatial Digital Twin and Collaborative Optimization, China University of Geosciences (Beijing), Beijing 100083, China
* CHENG Weiming, E-mail:

Received date: 2024-08-27

  Revised date: 2024-12-18

  Online published: 2025-03-25

Supported by

National Key Research and Development project(2022YFF0711400)

National Key Research and Development project(2019YFE0123300)

National Natural Science Foundation of China(41801361)

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摘要

【目的】月球撞击坑的识别与分类对探测器着陆点的选择和月球年龄的估算至关重要。然而，由于撞击作用形成了复杂的月貌形态，像素级不可再分的微观月貌特征的研究面临诸多挑战。基于此，对微观月貌特征的识别需要采用尺度自适应的方法，同时应充分考虑微观月貌特征来完善月球撞击坑分类图谱。【方法】本研究引入了一种基于地貌基元的尺度自适应算法，用于自动分类微观月貌。首先，该算法通过优化地形参数来定义月球地貌的局部三元模式，并依据优化后的局部三元模式确定月貌基元。然后，依据地形起伏度和坡度的规则将月貌基元进行聚合，以识别大范围内的月球撞击地貌单元。最后，基于月貌基元构建加加林环形山区域的月球撞击坑分类图谱。【结果】该方法提取了月球微观形貌基本单元，实现了多尺度撞击坑识别，精确率达到88.24%，召回率为84.96%， F1分数为86.57%。最终建立了撞击坑分类图谱，包括简单酒窝型、小规模碗型、小规模平底型、小规模中央峰型、中等平原型、中等中央峰型、大环状平原型和特大复杂型。【结论】该方法在识别撞击坑时展现了稳健性和高效率，并且识别和分类后的撞击坑为月球地貌学和地质学分析提供了多尺度的地貌单元。

关键词： 地貌基元; 撞击坑识别; 参数优化; 撞击坑分类; 多尺度; 撞击坑分类图谱; DEM

本文引用格式

王娇 , 李俊娇 , 芮琦瑶 , 程维明 . 基于月貌基元的月球撞击坑识别与分类[J]. 地球信息科学学报, 2025 , 27(4) : 820 -834 . DOI: 10.12082/dqxxkx.2025.240474

Abstract

[Objectives] The identification and classification of lunar impact craters are critical for selecting spacecraft landing sites and estimating the Moon's geological age. However, the complex morphological features created by impact processes post significant challenges to studying micro-scale lunar surface features, which are often indivisible at the pixel level. Addressing these challenges requires a scale-adaptive approach that incorporates micro-scale characteristics to refine lunar impact crater classification maps. [Methods] This study introduces a scale-adaptive algorithm based on geomorphons for the automatic classification of micro-scale lunar surface features. First, terrain parameters are optimized to define local ternary patterns of lunar geomorphology. These patterns are then used to determine lunar geomorphons. Next, the geomorphons are aggregated according to rules based on relief amplitude and slope to identify lunar impact geomorphic units on a larger scale. Finally, a classification map of lunar impact craters in the Gagarin Crater region is constructed using the identified geomorphons. [Results] The proposed method successfully identifies the optimal parameters for adaptively scaling lunar geomorphons by incorporating the unique characteristics of lunar surface features. Using a four-parameter constraint window, lunar geomorphons are refined at locally optimal spatial scales through the computation of local ternary patterns integrated with the theory of lunar geomorphological evolution. The results reveal that the generated maps of lunar geomorphons exhibit significant spatial aggregation, well-defined classification boundaries, and high accuracy in representing lunar impact craters. The method effectively captures the internal structural details of impact craters, providing a pixel-level depiction of their morphological features. The multi-scale identification of impact craters achieves a precision of 88.24%, a recall of 84.96%, and an F1 score of 86.57%. A classification schema for impact craters was established, including simple pit, small-scale bowl, small-scale flat bottom, small-scale central peak, medium flat bottom, medium central peak, large ring plain, and giant complex. [Conclusions] This method demonstrates robustness and high efficiency in crater identification, offering multi-scale geomorphological units and serving as a foundational tool for scale-based lunar scientific research. It provides technical support for identifying and classifying multi-scale lunar impact craters, contributing to advancements in lunar morphological and geological analysis.

Key words： geomorphons; impact crater recognition; parameter optimization; impact crater classification; multi-scale; classification schema of impact craters; DEM

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