A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images
Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes...
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MDPI AG
2024-12-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/1/26 |
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| author | Shuyun Liu Haifeng Zhao Zihao Yuan Liping Xiao Chengcheng Shen Xue Wan Xuhai Tang Lu Zhang |
| author_facet | Shuyun Liu Haifeng Zhao Zihao Yuan Liping Xiao Chengcheng Shen Xue Wan Xuhai Tang Lu Zhang |
| author_sort | Shuyun Liu |
| collection | DOAJ |
| description | Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes a machine-learning approach to predict extraterrestrial rock hardness using morphological features. A custom dataset of 1496 rock images, including granite, limestone, basalt, and sandstone, was created. Ten features, such as roundness, elongation, convexity, and Lab color values, were extracted for prediction. A foundational model combining Random Forest (RF) and Support Vector Regression (SVR) was trained through cross-validation. The output of this model was used as the input for a meta-model, undergoing linear fitting to predict Mohs hardness, forming the Meta-Random Forest and Support Vector Regression (MRFSVR) model. The model achieved an R<sup>2</sup> of 0.8219, an MSE of 0.2514, and a mean absolute error of 0.2431 during validation. Meteorite samples were used to validate the MRFSVR model’s predictions. The model is used to predict the hardness distribution of extraterrestrial rocks using images from the Tianwen-1 Mars Rover Navigation and Terrain Camera (NaTeCam) and a simulated lunar rock dataset from an open-source website. The results demonstrate the method’s potential for enhancing extraterrestrial exploration. |
| format | Article |
| id | doaj-art-d4b6d54f6a16455b8e2eae8eb2718ddc |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Aerospace |
| spelling | doaj-art-d4b6d54f6a16455b8e2eae8eb2718ddc2025-01-24T13:15:30ZengMDPI AGAerospace2226-43102024-12-011212610.3390/aerospace12010026A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital ImagesShuyun Liu0Haifeng Zhao1Zihao Yuan2Liping Xiao3Chengcheng Shen4Xue Wan5Xuhai Tang6Lu Zhang7University of Chinese Academy of Sciences, Beijing 100039, ChinaUniversity of Chinese Academy of Sciences, Beijing 100039, ChinaTechnology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, ChinaUniversity of Chinese Academy of Sciences, Beijing 100039, ChinaUniversity of Chinese Academy of Sciences, Beijing 100039, ChinaUniversity of Chinese Academy of Sciences, Beijing 100039, ChinaSchool of Civil Engineering, Wuhan University, Wuhan 430072, ChinaTechnology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, ChinaUnderstanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes a machine-learning approach to predict extraterrestrial rock hardness using morphological features. A custom dataset of 1496 rock images, including granite, limestone, basalt, and sandstone, was created. Ten features, such as roundness, elongation, convexity, and Lab color values, were extracted for prediction. A foundational model combining Random Forest (RF) and Support Vector Regression (SVR) was trained through cross-validation. The output of this model was used as the input for a meta-model, undergoing linear fitting to predict Mohs hardness, forming the Meta-Random Forest and Support Vector Regression (MRFSVR) model. The model achieved an R<sup>2</sup> of 0.8219, an MSE of 0.2514, and a mean absolute error of 0.2431 during validation. Meteorite samples were used to validate the MRFSVR model’s predictions. The model is used to predict the hardness distribution of extraterrestrial rocks using images from the Tianwen-1 Mars Rover Navigation and Terrain Camera (NaTeCam) and a simulated lunar rock dataset from an open-source website. The results demonstrate the method’s potential for enhancing extraterrestrial exploration.https://www.mdpi.com/2226-4310/12/1/26extraterrestrial explorationrock hardnessmachine learningimage recognitionmeta learningRandom Forest |
| spellingShingle | Shuyun Liu Haifeng Zhao Zihao Yuan Liping Xiao Chengcheng Shen Xue Wan Xuhai Tang Lu Zhang A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images Aerospace extraterrestrial exploration rock hardness machine learning image recognition meta learning Random Forest |
| title | A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images |
| title_full | A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images |
| title_fullStr | A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images |
| title_full_unstemmed | A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images |
| title_short | A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images |
| title_sort | machine learning approach for the autonomous identification of hardness in extraterrestrial rocks from digital images |
| topic | extraterrestrial exploration rock hardness machine learning image recognition meta learning Random Forest |
| url | https://www.mdpi.com/2226-4310/12/1/26 |
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