nishtha2405 / crism_ml

Python code for training machine learning models on CRISM

CRISM Machine Learning Toolkit

Introduction

This package demonstrates the utility of machine learning in two important tasks in hyperspectral image analysis: nonlinear noise removal (ratioing) and mineral classification.

We developed a model to classify spectra in satellite hyperspectral image from Mars, acquired by the CRISM experiment. Specifically, we implement a Hierarchical Bayesian Model (HBM) based on a Gaussian model for spectra, with a global Normal-Inverse Wishart prior and a separate local Normal prior for each image.

The HBM is used to identify pixels lacking definite spectral features (i.e., bland) and use them divide (ratio) the remaining spectra on an image column to remove nonlinear noise and distortions. A second model is trained on ratioed spectra to classify a set of 33 distinct mineral classes.

The code offers the following functionality:

• Train a HBM to classify bland pixels and minerals

• Preprocessing utilities to clean and ratio the spectra

• Plotting utilities to generate a false-color image, show predictions and per-region spectra

Usage

The code requires Python 3.7 or newer and it runs on Windows and Linux. You can install an environment with all the dependencies with:

conda env create -f environment.yml

A requirements.txt file is also available if you do not have Anaconda; run this in your virtual environment:

pip install -r requirements.txt

To use the package from any path, install it in your environment with the following command from the project root:

pip install -e .

This command will also take care of the project dependencies if you didn't install them and add the project to the python packages in editable mode.

Running example

To check that everything is working, download an image from the CRIM website and run the main script on it. The script witll train the models, preprocess the image and classify the pixels. A set of plots will be saved in the workdir/plot directory (using GIT bash):

# download dataset
mkdir -p datasets && cd datasets
curl -O http://cs.iupui.edu/~mdundar/CRISM/CRISM_bland_unratioed.mat
curl -O http://cs.iupui.edu/~mdundar/CRISM/CRISM_labeled_pixels_ratioed.mat
cd ..

# download image
curl -O https://pds-geosciences.wustl.edu/mro/mro-m-crism-3-rdr-targeted-v1/mrocr_2104/trdr/2010/2010_056/hrl00016cfe/hrl00016cfe_07_if181l_trr3.img
curl -O https://pds-geosciences.wustl.edu/mro/mro-m-crism-3-rdr-targeted-v1/mrocr_2104/trdr/2010/2010_056/hrl00016cfe/hrl00016cfe_07_if181l_trr3.lbl

python crism_ml/train.py hrl00016cfe_07_if181l_trr3 --plot

A detailed guide of all the steps performed by the classification script is available in tutorials/Training.ipynb.

Dataset

We released two datasets on the CRISM ML toolkit website, to train the bland pixel model and the mineral model. Download them to the datasets directory or pass the path to thetrain.py script using the --datapath argument.

The bland pixel dataset has the following variables:

Name	Size	Description
pixspec	337617×350	Unratioed spectra
im_names	340	List of CRISM image names, mapping to numerical ID
pixims	337617	Numerical ID of the original image
pixcrds	337617×2	(x,y) point coordinates in the original image

And the mineral dataset has the following structure:

Name	Size	Description
pixspec	592413×350	Ratioed spectra
pixlabs	592413	Mineral labels
im_names	77	List of CRISM image names, mapping to numerical ID
pixims	592413	Numerical ID of the original image
pixpat	592413	ID of the connected patch the pixel belongs to
pixcrds	337617×2	(x,y) point coordinates in the original image

License

The code is released under the Apache-2 License (see LICENSE.txt for details).

Citation

If you find this repository useful in your research, please cite:

@article{Plebani2022crism,
  title = {A machine learning toolkit for {CRISM} image analysis},
  journal = {Icarus},
  pages = {114849},
  year = {2022},
  issn = {0019-1035},
  doi = {https://doi.org/10.1016/j.icarus.2021.114849},
  url = {https://www.sciencedirect.com/science/article/pii/S0019103521004905},
  author = {Emanuele Plebani and Bethany L. Ehlmann and Ellen K. Leask and Valerie K. Fox and M. Murat Dundar},
}