行星探测特征信息提取与知识挖掘关键技术及应用研究

doi:10.11871/jfdc.issn.2096-742X.2025.04.001

数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (4): 3-19.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.04.001

doi: 10.11871/jfdc.issn.2096-742X.2025.04.001

• 专刊：空间科学大数据智能算法模型与工具 • 上一篇下一篇

行星探测特征信息提取与知识挖掘关键技术及应用研究

凌宗成^1,^*(),李勃¹,魏广飞^2,³,郭弟均⁴,吕英波¹,刘长卿¹,朱凯²,陈剑¹,赵强⁵,李静⁶,胡国平⁷,王娇⁸,刘建忠²

1.山东大学，空间科学与技术学院，空间科学研究院，山东威海 264209
2.中国科学院地球化学研究所，贵州贵阳 550081
3.深空探测实验室，安徽合肥 230026
4.中国科学院国家空间科学中心，太阳活动与空间天气全国重点实验室，北京 100190
5.商丘师范学院测绘与规划学院，河南商丘 476000
6.吉林大学地球探测科学与技术学院，吉林长春 130026
7.中山大学测绘科学与技术学院，广东珠海 519082
8.中国地质大学（北京）信息工程学院，北京 100083

收稿日期:2025-06-12 出版日期:2025-08-20 发布日期:2025-08-21
通讯作者: 凌宗成
作者简介:凌宗成，教授，山东大学空间科学与技术学院副院长，行星科学团队课题组长，教育部深空探测联合研究中心行星光谱与空间天气分中心副主任。主要从事行星科学与深空探测领域研究。
本文负责总体设计，内容撰写。
LING Zongcheng, Professor, Principal Investigator of the Planetary Science Team at Shandong University, Vice Dean of the School of Space Science and Technology, and Deputy Director of the Planetary Spectroscopy and Space Weather Sub-center of the Deep Space Exploration Joint Center of the Ministry of Education. Mainly engaged in research in the fields of planetary science and deep space exploration.
In this paper, he is primarily responsible for overall research and content writing.
E-mail: zcling@sdu.edu.cn
基金资助:
国家重点研发计划项目(2022YFF0711400);空间科学大数据智能管理与分析挖掘关键技术及应用

Research on Key Technologies and Applications of Feature Extraction and Knowledge Mining in Planetary Exploration

LING Zongcheng^1,^*(),LI Bo¹,WEI Guangfei^2,³,GUO Dijun⁴,LYU Yingbo¹,LIU Changqing¹,ZHU Kai²,CHEN Jian¹,ZHAO Qiang⁵,LI Jing⁶,HU Guoping⁷,WANG Jiao⁸,LIU Jianzhong²

1. School of Space Sciences and Technology, Institute of Space Sciences, Shandong University, Weihai, Shandong 264209, China
2. Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, Guizhou 550081, China
3. Deep Space Exploration Laboratory, Hefei, Anhui 230026, China
4. State Key Laboratory of Solar Activity and Space Weather, National Space Science Center, ;Chinese Academy of Sciences, Beijing, 100190, China
5. Department of Surveying and Planning, Shangqiu Normal University, Shangqiu, Henan 476000, China
6. College of Geoexploration Science and Technology, Jinlin University, Changchun, Jilin 130026, China
7. School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, Guangdong 519082, China
8. School of Information and Engineering, China University of Geosciences (Beijing), Beijing 100083, China

Received:2025-06-12 Online:2025-08-20 Published:2025-08-21
Contact: LING Zongcheng

摘要/Abstract

摘要：

【目的】本文针对行星探测数据的深度挖掘和智能提取的迫切需求，基于国内外行星探测获取的海量遥感数据，开展了行星探测特征信息提取与知识挖掘的关键技术及其应用研究。【方法】突破了多源、异构的行星数据的重构融合和可视化技术，克服了单一传感器成像信息不足的问题，可生成具有丰富空间和光谱信息的高质量遥感图像。建立了基于可见近红外光谱探测数据的物质成分反演模型，可提取月球及火星矿物光谱特征参量并反演月表元素、矿物的含量与分布。开发了融合多源数据的月壤厚度反演与次表层结构反演算法，利用微波和雷达数据获取月壤厚度及其物理性质，可对次表层结构和地层信息进行分析。利用月球和火星的影像及高程数据，实现了表面多尺度地形因子计算和基于深度学习的典型形貌特征自动提取、绝对模式年龄计算和地质要素制图功能。【结果】在此基础上，实现形貌要素、物质成分信息、次表层结构的集成平台展示和互操作分析，研制了具有自主知识产权的行星数据分析挖掘软件工具。该工具将在国家空间科学数据中心公开部署，并在山东大学威海行星数据系统（PDS）实验室镜像发布，以支撑行星数据制图和地质演化等相关研究。

关键词: 行星探测数据, 知识挖掘, 物质成分, 次表层结构, 形貌要素, 可视化, 重构融合

Abstract:

[Purpose] This article focuses on the demand for deep mining and intelligent feature extraction of planetary exploration data. It investigates key technologies and applications for extracting feature information and knowledge mining from massive remote sensing data obtained during various planetary explorations. [Method] This study breaks through the challenges of reconstructing, fusing, and visualizing multi-source and heterogeneous planetary data, addressing the limitations of insufficient imaging information from single sensor to generate high-quality remote sensing images rich in spatial and spectral information. Spectral characteristic parameters of lunar and martian minerals are extracted from visible near-infrared spectroscopic data, enabling the inversion of the content and distribution of elements and minerals on the lunar and martian surface. Additionally, microwave and radar data are utilized to obtain information regarding the thickness and physical properties of lunar regolith, along with the subsurface structure of typical regions on the Moon and Mars. By employing images and elevation data of the Moon and Mars, multi-scale terrain factors on the surface are calculated, and typical morphological features are automatically extracted using deep learning techniques. [Results] Finally, an integrated platform for displaying and analyzing morphological features, material composition information, and subsurface structures has been established. Furthermore, a planetary data analysis and mining software tool with independent intellectual property rights is developed, which will be released by National Space Science Data Center and Planetary Data System (PDS) Laboratory of Shandong University at Weihai, to support planetary data mapping and geological evolution researches.

Key words: planetary exploration data, knowledge mining, chemical and mineralogical composition, subsurface structure, morphological features, visualization, reconstruction and fusion

凌宗成, 李勃, 魏广飞, 郭弟均, 吕英波, 刘长卿, 朱凯, 陈剑, 赵强, 李静, 胡国平, 王娇, 刘建忠. 行星探测特征信息提取与知识挖掘关键技术及应用研究[J]. 数据与计算发展前沿, 2025, 7(4): 3-19.

LING Zongcheng, LI Bo, WEI Guangfei, GUO Dijun, LYU Yingbo, LIU Changqing, ZHU Kai, CHEN Jian, ZHAO Qiang, LI Jing, HU Guoping, WANG Jiao, LIU Jianzhong. Research on Key Technologies and Applications of Feature Extraction and Knowledge Mining in Planetary Exploration[J]. Frontiers of Data and Computing, 2025, 7(4): 3-19, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2025.04.001.

图/表 18

图1

图2

图3

图4

图5

图6

图7

图8

表1

图9

图10

图11

图12

图13

图14

图15

图16

图17

参考文献 45

[1]	GHASSEMIAN H. A review of remote sensing image fusion methods[J]. Information Fusion, 2016, 32: 75-89.
[2]	CORLEY L M, MCGOVERN P J, KRAMER G Y, et al. Olivine-bearing lithologies on the Moon: Constraints on origins and transport mechanisms from M3 spectroscopy, radiative transfer modeling, and GRAIL crustal thickness[J]. Icarus, 2018, 300: 287-304.
[3]	BISHOP J L, GROSS C, DANIELSEN J, et al. Multiple mineral horizons in layered outcrops at Mawrth Vallis, Mars, signify changing geochemical environments on early Mars[J]. Icarus, 2020, 341: 113634.
[4]	FA W, JIN Y Q. A primary analysis of microwave brightness temperature of lunar surface from Chang-E 1 multi-channel radiometer observation and inversion of regolith layer thickness[J]. Icarus, 2010, 207(2): 605-615.
[5]	NICKERSON R D, BART G D, LAWDER M T, et al. GLOBAL LUNAR REGOLITH DEPTHS REVEALED[J]. 42nd Lunar and Planetary Science Conference, 2011: 2607.
[6]	DI K, SUN S, YUE Z, et al. Lunar regolith thickness determination from 3D morphology of small fresh craters[J]. Icarus, 2016, 267: 12-23.
[7]	YUE Z, DI K, LIU Z, et al. Lunar regolith thickness deduced from concentric craters in the CE-5 landing area[J]. Icarus, 2019, 329: 46-54.
[8]	VENKATRAMAN J, HORVATH T, POWELL T M, et al. Statistical estimates of rock-free lunar regolith thickness from diviner[J]. Planetary and Space Science, 2023, 229: 105662.
[9]	ZHIGUO M, YI X, ZHANCHUAN C, et al. Influence of lunar topography on simulated surface temperature[J]. Advances in Space Research, 2014, 54(10): 2131-2139.
[10]	WANG R, SU Y, DING C, et al. A Novel Approach for Permittivity Estimation of Lunar Regolith Using the Lunar Penetrating Radar Onboard Chang’E-4 Rover[J]. 2021, 13(18): 3679.
[11]	FA W, ZHU M H, LIU T, et al. Regolith stratigraphy at the Chang’E-3 landing site as seen by lunar penetrating radar[J]. Geophysical Research Letters, 2015, 42(23): 179-187.
[12]	CAMPBELL B A, HAWKE B R, THOMPSON T W. Regolith composition and structure in the lunar maria: Results of long-wavelength radar studies[J]. J. Geophys. Res., 1997, 102(E8): 19307-19320.
[13]	SHKURATOV Y. Regolith Layer Thickness Mapping of the Moon by Radar and Optical Data[J]. Icarus, 2001, 149(2): 329-338.
[14]	LAI J, XU Y, ZHANG X, et al. Comparison of Dielectric Properties and Structure of Lunar Regolith at Chang’e-3 and Chang’e-4 Landing Sites Revealed by Ground-Penetrating Radar[J]. Geophysical Research Letters, 2019, 46(22): 12783-12793.
[15]	HELFENSTEIN P, SHEPARD M K. Submillimeter-scale topography of the lunar regolith[J]. Icarus, 1999, 141(1): 107-131.
[16]	GUO D, FA W, WU B, et al. Millimeter- to Decimeter-Scale Surface Roughness of the Moon at the Chang’e-4 Exploration Region[J]. Geophysical Research Letters, 2021, 48(19): e2021GL094931.
[17]	OROSEI R, BIANCHI R, CORADINI A, et al. Self-affine behavior of Martian topography at kilometer scale from Mars Orbiter Laser Altimeter data[J]. J Geophys Res Planets, 2003, 108(E4).
[18]	KRESLAVSKY M A, HEAD J W. North-south topographic slope asymmetry on Mars: Evidence for insolation-related erosion at high obliquity[J]. Geophys Res Lett, 2003, 30(15): 1815.
[19]	KRESLAVSKY M A, HEAD J W, NEUMANN G A, et al. Lunar topographic roughness maps from Lunar Orbiter Laser Altimeter (LOLA) data: Scale dependence and correlation with geologic features and units[J]. Icarus, 2013, 226(1): 52-66.
[20]	LI B, ZHANG J, YUE Z, et al. Deriving terrain factors from high-resolution lunar images: A case study of the Mons Rümker Region[J]. Geomorphology, 2020, 358.
[21]	WU B, DONG J, WANG Y, et al. Landing Site Selection and Characterization of Tianwen-1 (Zhurong Rover) on Mars[J]. J Geophys Res Planets, 2022, 127(4).
[22]	DELATTE D M, CRITES S T, GUTTENBERG N, et al. Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era[J]. Advances in Space Research, 2019, 64(8): 1615-1628.
[23]	CHRISTIAN J A, DERKSEN H, WATKINS R. Lunar Crater Identification in Digital Images[J]. J Astronaut Sci, 2021, 68(4): 1056-144. doi: 10.1007/s40295-021-00287-8 pmid: 35001965
[24]	LEE C. Automated crater detection on Mars using deep learning[J]. Planet Space Science, 2019.
[25]	FAIRWEATHER J, LAGAIN A, SERVIS K, et al. Using an Automated Crater Detection Algorithm as a Tool for Mapping Secondary Crater Clusters on the Moon: Chang’E 5 Landing Site[J]. LPI Contributions, 2022, 2678: 1925.
[26]	RUBANENKO L, PéREZ-LóPEZ S, SCHULL J, et al. Automatic Detection and Segmentation of Barchan Dunes on Mars and Earth Using a Convolutional Neural Network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 9364-9371.
[27]	WANG Y, WU B. Active Machine Learning Approach for Crater Detection From Planetary Imagery and Digital Elevation Models[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8): 5777-5789.
[28]	NEUKUM G, IVANOV B A, HARTMANN W K. Cratering Records in the Inner Solar System in Relation to the Lunar Reference System[J]. Space Science Reviews, 2001, 96(1): 55-86.
[29]	LAGAIN A, SERVIS K, BENEDIX G K, et al. Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods[J]. Earth and Space Science, 2021, 8(2): e2020EA001598.
[30]	ANG C, ZHAO H, BRUZZONE L, et al. Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning[J]. Nat Commun, 2020, 11(1): 6358.
[31]	YANG H, XU X, MA Y, et al. CraterDANet: A Convolutional Neural Network for Small-Scale Crater Detection via Synthetic-to-Real Domain Adaptation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-12.
[32]	刘丹, 甘红, 魏广飞, 等. 基于光照和坡度约束的月球南极着陆选址分析[J]. 深空探测学报(中英文), 2023, 10(5): 544-556.
[33]	LEMELIN M, LUCEY P G, CAMON A. Compositional Maps of the Lunar Polar Regions Derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter Data[J]. The Planetary Science Journal, 2022, 3(3): 63.
[34]	凌宗成, 张江, 刘建忠, 等. 嫦娥一号干涉成像光谱仪数据再校正与全月铁钛元素反演[J]. 岩石学报, 2016, 32(1): 87-98.
[35]	WANG Y, CAO H, CHEN J, et al. New maps of mafic mineral abundances in global mare units on the Moon[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2025, 224: 348-360.
[36]	WEI G, LI X, GAN H, et al. Retrieval of lunar polar heat flow from Chang’E-2 microwave radiometer and Diviner observations[J]. Frontiers in Astronomy and Space Sciences, 2023, 10: 1179558.
[37]	GAN H, ZHAO C, WEI G, et al. Numerical Simulation of the Lunar Polar Environment: Implications for Rover Exploration Challenge[J]. Aerospace, 2023, 10(7): 598.
[38]	WEI G, LI X, ZHANG W, et al. Illumination conditions near the Moon’s south pole: Implication for a concept design of China’s Chang’E-7 lunar polar exploration[J]. Acta Astronautica, 2023, 208: 74-81.
[39]	GAN H, WEI G, ZHANG X, et al. Experimental study on electrostatic migration of different mineral particles composing lunar dust under electron irradiation[J]. Frontiers in Astronomy and Space Sciences, 2023, 10: 1213294.
[40]	GAN H, WEI G, ZHANG W, et al. Lunar polar illumination and electrical field environment simulation based on a conceptual design for China’ s Chang’ E-7 mission[J]. SCIENTIA SINICA Physica, Mechanica & Astronomica, 2023, 53(4): 24 9611.
[41]	YANG W, HU G, YANG F, et al. Inversion of the Lunar Subsurface Rock Abundance Using CE-2 Microwave Brightness Temperature Data[J]. Remote Sensing, 2023, 15(20): 4895.
[42]	LI J, CHEN Y, ZENG Z. Estimated Lunar Regolith Structure Based on the Least-Squares Kirchhoff Migration of CE-3 Lunar Penetrating Radar Data[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5): 816-820.
[43]	LI B, QU S, LING Z, et al. A Preliminary Study on the Identification and Spatio-Temporal Characteristics of Martian Atmospheric Eddies[J]. Journal of Geophysical Research: Planets, 2024, 129(2): e2023JE00 7937.
[44]	YANG Y, WANG Y, LI B, et al. Variation of crater morphological parameters in the landing area of Tianwen-1: relationships with the geological environment and climate change[J]. Earth, Planets and Space, 2024, 76(1): 19.
[45]	GUO D, BAO Y, LIU Y, et al. Geological investigation of the lunar Apollo basin: From surface composition to interior structure[J]. Earth and Planetary Science Letters, 2024, 646: 118986.

光谱参数名称	光谱参数物理意义
BD_1330	斜长石Fe²⁺吸收参数
LCPINDEX	低钙辉石参数
HCPINDEX	高钙辉石参数
OLINDEX	橄榄石参数
BD_2265	黄钾铁矾、水铝矿、绿脱石
BD_2290	Mg/Fe-OH或CO₂冰
BD_2355	绿泥石、葡萄石、绿纤石
MIN_2200	高岭石
MIN_2250	蛋白石
BD_2100	一水硫酸盐中的H₂O
BD_2230	羟基硫酸铁
SINDEX	含水硫酸盐
BD_2265	黄钾铁矾、水铝矿、酸性淋滤的绿脱石
BD_2500	碳酸镁
MIN_2295_2480	碳酸镁
MIN_2345_2537	碳酸钙/铁
BD_1400	H₂O和-OH
BD_1750	H₂O
BD_1900	H₂O
BD_1500	H₂O冰
BD_1435	CO₂冰
BD_2290	Mg/Fe-OH或CO₂冰
BD_2165	Al-OH
BD_2190	Al-OH
BD_2210	Al-OH
BD_2250	Al-OH和Si-OH
BD_2250	Al-OH和Si-OH

行星探测特征信息提取与知识挖掘关键技术及应用研究

Research on Key Technologies and Applications of Feature Extraction and Knowledge Mining in Planetary Exploration

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 18

参考文献 45

相关文章 11

编辑推荐

Metrics

本文评价

[1]	刘方超,张立,郭弟均,陈剑,吕英波,凌宗成,李博然,李心语,马云龙. 月球撞击坑形态特征的多维度聚类方法研究[J]. 数据与计算发展前沿, 2025, 7(4): 89-100.
[2]	钟佳,邹自明. 基于直接体可视化方法提取磁层顶结构[J]. 数据与计算发展前沿, 2025, 7(4): 79-88.
[3]	崔潇骁, 赵玉立, 修涵文, 薛晓光, 黄咏政, 何晓辉, 朱江. 面向非规则体离散的高效物质点生成技术研究[J]. 数据与计算发展前沿, 2025, 7(3): 174-184.
[4]	余敏槠, 王杨, 顾睿琪, 单桂华, 金钟. TSPVS：时序学者画像可视化系统[J]. 数据与计算发展前沿, 2024, 6(1): 136-149.
[5]	赵一宁, 肖海力. 国家高性能计算环境运行状态诊断系统[J]. 数据与计算发展前沿, 2024, 6(1): 57-67.
[6]	刘嘉琪,杨斌艳. 我国人工智能与社会科学耦合发展的热点与趋势研究——基于CiteSpace的文献计量分析[J]. 数据与计算发展前沿, 2022, 4(6): 77-91.
[7]	石晓霖,毕重科,黄元琪,邓亮,王岳青,王昉. 基于虚拟视点的大规模数值模拟的可交互式原位可视化[J]. 数据与计算发展前沿, 2021, 3(4): 30-43.
[8]	申丽铭,郭雨蒙,王文珂. 基于矢量完全信息熵的流线并行分布方法[J]. 数据与计算发展前沿, 2021, 3(4): 44-53.
[9]	董家源,杨小渝. 材料数据挖掘与机器学习工具的集成与优化[J]. 数据与计算发展前沿, 2020, 2(4): 105-120.
[10]	单桂华,刘俊,李观,高阳,徐涛,田东. 面向大规模数据的科学可视化系统GPVis[J]. 数据与计算发展前沿, 2019, 1(1): 46-62.
[11]	周园春,常青玲,杜一. SKS：一种科技领域大数据知识图谱平台 ^*[J]. 数据与计算发展前沿, 2019, 1(1): 82-93.