Machine learning in Planetary Science: compressing Pluto images with scikit-learn and PCA

In a previous post I showed some of the beautiful new images of Pluto from New Horizon’s mission,  coloured using the new Matplotlib perceptual colormaps:


More recently I was experimenting with Principal Component Analysis in scikit-learn, and one of the things I used it for was compression of some of these Pluto images. Below is an example of the first two components from the False Color Pluto image:


You can take a look at the Python code available on this Jupyter Notebook. There are of course better ways of compressing images, but this was a fun way to play around with PCA.

In a follow-up post I will use image registration and image processing techniques to reproduce from the raw channels NASA’s Psychedelic Pluto Image.



New Horizons truecolor Pluto recolored in Viridis and Inferno

Oh, the new, perceptual MatplotLib colormaps…..

Here’s one stunning, recent Truecolor image of Pluto from the New Horizons mission:


Original image: The Rich Color Variations of Pluto. Credit: NASA/JHUAPL/SwRI. Click on the image to view the full feature on New Horizon’s site

Below, I recolored using two of the new colormaps:


Recolored images: I like Viridis, by it is Inferno that really brings to life this image, because of its wider hue and lightness range!