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:

colormappedNew_Horizons_Pluto

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:

first_two

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.

 

 

One response to “Machine learning in Planetary Science: compressing Pluto images with scikit-learn and PCA

  1. Pingback: Machine learning in geoscience and planetary science with scikit-learn: series outline | MyCarta·

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