First version of a new toolkit to perform unsupervised machine learning (clustering) on CRISM near-infrared hyperspectral data on Mars. The toolkit will:
- Read and visualise CRISM data;
- Perform data preprocessing;
- Reduce the dimensionality of the data through projection-based (PCA) and, additionally, manifold-based (UMAP) techniques;
- Perform clustering on the data through the available methods (k-Means and Gaussian Mixture Models);
- Extract and visualise the mean spectra of each cluster;
- Evaluate cluster quality with the Silhouette score.
The tool has been developed with the support of Dr. Mario D'Amore (@kidpixo)
Contributors
kidpixo