Feature Extraction and Classification from Planetary Science Datasets
  enabled by Machine Learning
Conor Nixon Zachary Yahn Ethan Duncan Ian Neidel Alyssa Mills Benoît Seignovert Andrew Larsen Kathryn Gansler Charles Liles Catherine Walker Douglas Trent John Santerre
In this paper we present two examples of recent investigations that we have
undertaken, applying Machine Learning (ML) neural networks (NN) to image
datasets from outer planet missions to achieve feature recognition. Our first
investigation was to recognize ice blocks (also known as rafts, plates,
polygons) in the chaos regions of fractured ice on Europa. We used a transfer
learning approach, adding and training new layers to an industry-standard Mask
R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks
in a training dataset. Subsequently, the updated model was tested against a new
dataset, achieving 68% precision. In a different application, we applied the
Mask R-CNN to recognize clouds on Titan, again through updated training
followed by testing against new data, with a precision of 95% over 369 images.
We evaluate the relative successes of our techniques and suggest how training
and recognition could be further improved. The new approaches we have used for
planetary datasets can further be applied to similar recognition tasks on other
planets, including Earth. For imagery of outer planets in particular, the
technique holds the possibility of greatly reducing the volume of returned
data, via onboard identification of the most interesting image subsets, or by
returning only differential data (images where changes have occurred) greatly
enhancing the information content of the final data stream.