Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data
Abstract This paper addresses some of the challenges in automating mineral mapping from CRISM hyperspectral data using two ML algorithms, namely, Random Forest (RF) and Gradient Boosted Trees (GBTree) algorithms. An interpretable framework using tree‐ensemble classification and SHapely Additive exPl...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000391 |
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| author | Sandeepan Dhoundiyal Moni Shankar Dey Shashikant Singh Pattathal V. Arun Guneshwar Thangjam Alok Porwal |
| author_facet | Sandeepan Dhoundiyal Moni Shankar Dey Shashikant Singh Pattathal V. Arun Guneshwar Thangjam Alok Porwal |
| author_sort | Sandeepan Dhoundiyal |
| collection | DOAJ |
| description | Abstract This paper addresses some of the challenges in automating mineral mapping from CRISM hyperspectral data using two ML algorithms, namely, Random Forest (RF) and Gradient Boosted Trees (GBTree) algorithms. An interpretable framework using tree‐ensemble classification and SHapely Additive exPlanations (SHAP) is implemented to interpret the model decisions. SHAP explanations quantify the influence of diagnostic absorption features, demonstrating that classifiers rely on physically significant spectral features, rather than artifacts. Novel metrics: “Physically Significant Precision,” “Physically Significant Recall,” and “Physically Significant F‐measure” quantify the classifier's expected performance on unseen data. RF outperforms GBTree, and thus is used to develop a novel framework for mineral mapping from CRISM data, demonstrated on five CRISM datacubes. This amalgamation of Random Forest and SHAP addresses limitations associated with existing CRISM classification methods, offering stability during training, reduced manual intervention, and interpretability while achieving a Kappa (κ) of 0.91 over the CRISM Machine Learning Toolkit's mineral data set with ∼470,000 labeled spectra. |
| format | Article |
| id | doaj-art-8cbec6a79d284efaad87ba1aea683ea2 |
| institution | OA Journals |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-8cbec6a79d284efaad87ba1aea683ea22025-08-20T02:21:10ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2024JH000391Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral DataSandeepan Dhoundiyal0Moni Shankar Dey1Shashikant Singh2Pattathal V. Arun3Guneshwar Thangjam4Alok Porwal5Centre of Studies in Resources Engineering Indian Institute of Technology Bombay Mumbai IndiaCentre of Studies in Resources Engineering Indian Institute of Technology Bombay Mumbai IndiaCentre of Studies in Resources Engineering Indian Institute of Technology Bombay Mumbai IndiaComputer Science and Engineering Group Indian Institute of Information Technology Sri City Sri City IndiaSchool Earth & Planetary Sciences National Institute of Science Education and Research Bhubhaneshwar IndiaCentre of Studies in Resources Engineering Indian Institute of Technology Bombay Mumbai IndiaAbstract This paper addresses some of the challenges in automating mineral mapping from CRISM hyperspectral data using two ML algorithms, namely, Random Forest (RF) and Gradient Boosted Trees (GBTree) algorithms. An interpretable framework using tree‐ensemble classification and SHapely Additive exPlanations (SHAP) is implemented to interpret the model decisions. SHAP explanations quantify the influence of diagnostic absorption features, demonstrating that classifiers rely on physically significant spectral features, rather than artifacts. Novel metrics: “Physically Significant Precision,” “Physically Significant Recall,” and “Physically Significant F‐measure” quantify the classifier's expected performance on unseen data. RF outperforms GBTree, and thus is used to develop a novel framework for mineral mapping from CRISM data, demonstrated on five CRISM datacubes. This amalgamation of Random Forest and SHAP addresses limitations associated with existing CRISM classification methods, offering stability during training, reduced manual intervention, and interpretability while achieving a Kappa (κ) of 0.91 over the CRISM Machine Learning Toolkit's mineral data set with ∼470,000 labeled spectra.https://doi.org/10.1029/2024JH000391hyperspectral remote sensingCRISMmineral mappingMarsExplainbale AI |
| spellingShingle | Sandeepan Dhoundiyal Moni Shankar Dey Shashikant Singh Pattathal V. Arun Guneshwar Thangjam Alok Porwal Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data Journal of Geophysical Research: Machine Learning and Computation hyperspectral remote sensing CRISM mineral mapping Mars Explainbale AI |
| title | Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data |
| title_full | Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data |
| title_fullStr | Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data |
| title_full_unstemmed | Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data |
| title_short | Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data |
| title_sort | explainable machine learning for mapping minerals from crism hyperspectral data |
| topic | hyperspectral remote sensing CRISM mineral mapping Mars Explainbale AI |
| url | https://doi.org/10.1029/2024JH000391 |
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