CRISM ML dataset

This dataset is required to train the models in the CRISM ML toolbox [1].

In the project, we demonstrate the utility of machine learning in two essential CRISM analysis tasks: nonlinear noise removal and mineral classification. We train a hierarchical Bayesian model for estimating distributions of spectral patterns on pixel-scale training data collected from dozens of well-characterized CRISM images. 

The following files are included:

CRISM_bland_unratioed.mat: unratioed training spectra for bland pixels.

CRISM_labeled_pixels_ratioed.mat: ratioed training spectra for mineral classes.

CRISM_labeled_pixel_patterns.pdf: visualization of the training segmentation maps and average spectra.

The training spectra are in Matlab v7.3 (and newer) format. To load them in Python, use the mat73 library, because scipy doesn't support the format.

The bland unratioed spectra have the following variables:

Name
Size
Description

pixspec
337 617 × 350
Unratioed spectra

im_names
340
List of CRISM image names, mapping them to numerical IDs

pixims
337 617
Numerical ID of the image the spectrum is from

pixcrds
337 617 × 2
(x,y) coordinates of the points in the original image

The labeled ratioed pixels have the following variables:

Name
Size
Description

pixspec
592 413 × 350
Ratioed spectra

pixlabs
592 413
Mineral labels

im_names
77
List of CRISM image names, mapping them to numerical IDs

pixims
592 413
Numerical ID of the image the spectrum is from

pixpat
592 413
ID of the connected patch in the image the pixel belongs to

pixcrds
592 413 × 2
(x,y) coordinates of pixels in their respective image

Citation (cite this paper when using the data):

Plebani, E., Ehlmann, B. L., Leask, E. K., Fox, V. K., & Dundar, M. M. (2022). A machine learning toolkit for CRISM image analysis. Icarus, 376, 114849.