A Multi-Dimensional Clustering Method for Morphological Characterization of Lunar Impact Craters

doi:10.11871/jfdc.issn.2096-742X.2025.04.008

Abstract

Abstract:

[Purpose] Lunar impact craters are important landmarks for studying the geologic evolution of the lunar surface, while systematically classifying and morphologically analyzing impact craters with the support of high-resolution image data is still an important research topic. [Method] In this study, based on the LROC NAC image data near the Chang'e-3 landing site, a k-means clustering method without training is proposed for automatic classification of impact craters with similar morphological features. Firstly, feature information in five dimensions, including mean, variance, entropy, contrast, and scale, is extracted from each image. Secondly, the optimal number of clusters, k=8, is determined by four methods, namely, the elbow method, the silhouette coefficient method, the Gap statistic, and hierarchical clustering. Thirdly, the feature information and the k are input into the k-means algorithm for clustering of the impact crater images. Finally, the images are clustered by drawing a cluster feature scatter plot and example images, etc. to visualize and analyze the clustering results. [Results] Based on the clustering results, the morphological features of different types of impact craters are analyzed in detail, revealing the potential significance of different morphological categories of impact craters in the geological evolution of the lunar surface. [Conclusion] This study provides a new method for the automated classification and morphological characterization of lunar impact craters, which provides important support for an in-depth understanding of lunar surface geological processes.

Key words: lunar impact crater, clustering, k-means, multidimensional features, visualization

LIU Fangchao, ZHANG Li, GUO Dijun, CHEN Jian, LYU Yingbo, LING Zongcheng, LI Boran, LI Xinyu, MA Yunlong. A Multi-Dimensional Clustering Method for Morphological Characterization of Lunar Impact Craters[J]. Frontiers of Data and Computing, 2025, 7(4): 89-100, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2025.04.008.

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	平均值	最大值	最小值
马氏距离	4.3292	1.4271	8.9489
对应的簇	—	簇0和簇4	簇1和簇5

类别	A	B	C	D	E	F	G	H
数量	210	114	58	252	268	33	257	7
占比	17.5%	9.5%	4.8%	21.0%	22.4%	2.8%	21.4%	0.6%
均值	0.36	0.45	0.33	0.22	0.39	0.61	0.26	0.41
方差	0.01	0.04	0.02	0.01	0.02	0.07	0.01	0.04
熵	6.50	7.34	7.11	6.23	6.98	7.28	6.70	7.48
对比度	0.11	0.20	0.14	0.08	0.15	0.26	0.11	0.19