CRISM Machine Learning Toolkit
In this project we demonstrate the utility of machine learning in two essential
CRISM analysis tasks: nonlinear noise removal and mineral classification. We propose a simple yet effective hierarchical Bayesian model for estimating distributions of spectral patterns and extensively validate this model for mineral classification on several test images. We package implemented scripts, documentation illustrating use cases, and pixel-scale training data collected from dozens of well-characterized
CRISM images into a new toolkit. We hope that this new toolkit will provide advanced and effective processing tools and improve
planetary community's ability to map compositional units in remote sensing data quickly, accurately, and at scale.
The toolkit with a user friendly Python notebook, source codes, and documentation is hosted at
GitHub. To save space the training datasets are not
included in the package and have to be downloaded from
Emanuele Plebani, Bethany L. Ehlmann, Ellen K. Leask,
Valerie K. Fox, M. Murat Dundar, "A Machine Learning
Toolkit for CRISM Image Analysis", Icarus, January 2022.
The development of the machine
learning algorithms in the toolkit was sponsored by
the National Science Foundation (NSF) under Grant
Number IIS-1252648 (CAREER). The content is solely
the responsibility of the authors and does not
necessarily represent the official views of NSF.
Toolkit development and
validation was sponsored by NASA under Grant Number
mdundar at iupui dot edu