Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data

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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Bibliographic Details
Main Authors: Sandeepan Dhoundiyal, Moni Shankar Dey, Shashikant Singh, Pattathal V. Arun, Guneshwar Thangjam, Alok Porwal
Format: Article
Language:English
Published: Wiley 2025-06-01
Series:Journal of Geophysical Research: Machine Learning and Computation
Subjects:
hyperspectral remote sensing
CRISM
mineral mapping
Mars
Explainbale AI
Online Access:https://doi.org/10.1029/2024JH000391
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https://doi.org/10.1029/2024JH000391

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