A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang'e-2 Lunar Microwave Sounder (CELMS) Data

被引:3
|
作者
Li, Yu [1 ]
Yuan, Zifeng [1 ]
Meng, Zhiguo [2 ]
Ping, Jinsong [3 ,4 ]
Zhang, Yuanzhi [3 ,4 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[2] Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun 130026, Peoples R China
[3] Chinese Acad Sci, Key Lab Lunar & Deep Space Explorat, Natl Astron Observ, Beijing 100101, Peoples R China
[4] Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China
关键词
Mare Fecunditatis; brightness temperature; feature assessment; mare basalt; machine learning; dimension reduction; IMBRIUM; CLASSIFICATION; FECUNDITATIS;
D O I
10.3390/rs15071910
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The brightness temperature (T-B) features extracted from Chang'e-2 Lunar Microwave Sounder (CELMS) data represent the passive microwave thermal emission (MTE) from the lunar regolith at different depths. However, there have been few studies assessing the importance and contribution of each T-B feature for mapping mare basalt units. In this study, a unified framework of T-B features analysis is proposed through a case study of Mare Fecunditatis, which is a large basalt basin on the eastern nearside of the Moon. Firstly, T-B maps are generated from original CELMS data. Next, all T-B features are evaluated systematically using a range of analytical approaches. The Pearson coefficient is used to compute the correlation of features and basalt classes. Two distance metrics, normalized distance and J-S divergence, are selected to measure the discrimination of basalt units by each T-B feature. Their contributions to basalt classification are quantitatively evaluated by the ReliefF method and out-of-bag (OOB) importance index. Then, principal component analysis (PCA) is applied to reduce the dimension of T-B features and analyze the feature space. Finally, a new geological map of Mare Fecunditatis is generated using CELMS data based on a random forest (RF) classifier. The results will be of great significance in utilizing CELMS data more widely as an additional tool to study the geological structure of the lunar basalt basin.
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页数:27
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