Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data

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 of 0.91 over the CRISM Machine Learning Toolkit's mineral data set with 470,000 labeled spectra.