Framework for Processing of CRISM Hyperspectral Data for Global Martian Mineralogy

Hyperspectral data from CRISM have proven invaluable for analyzing the mineralogical composition of the Martian surface. However, processing such datasets remains challenging due to their high dimensionality and systematic noise, such as striping artifacts caused by the pushbroom imaging technique. Building on previous research, this study introduces a framework that forms the basis for an automated pipeline that combines preprocessing, dimensionality reduction using UMAP, k-means clustering, and an adaptive stripe correction filter to generate mineral maps of the Martian surface. Additionally, the pipeline integrates a noise variance estimation step based on PCA to assess the feasibility and expected efficacy of stripe removal before applying the filter. We validate the methodology across multiple CRISM datasets, including regions such as Jezero Crater, Nili Fossae, and Mawrth Vallis. Comparative analyses using metrics such as the CH index, DB index, and SC demonstrate improved clustering performance and robust mineralogical mapping, which indicates a step toward more reliable and automated clustering of CRISM data. Furthermore, the pipeline leverages spectral libraries for automated mineral classification, yielding results comparable to expert-defined maps while addressing discrepancies caused by residual noise or clustering limitations. This study represents a step toward fully automated, scalable geospatial analysis of CRISM Martian surface data, offering a robust framework for processing large hyperspectral datasets and supporting future planetary exploration missions. In the future, we intend to deploy an automated analysis pipeline as a freely accessible web service.