  Craters are among the most studied geomorphic features in the Solar System
because they yield important information about the past and present geological
processes and provide information about the relative ages of observed geologic
formations. We present a method for automatic crater detection using advanced
machine learning to deal with the large amount of satellite imagery collected.
The challenge of automatically detecting craters comes from their is complex
surface because their shape erodes over time to blend into the surface.
Bandeira provided a seminal dataset that embodied this challenge that is still
an unsolved pattern recognition problem to this day. There has been work to
solve this challenge based on extracting shape and contrast features and then
applying classification models on those features. The limiting factor in this
existing work is the use of hand crafted filters on the image such as Gabor or
Sobel filters or Haar features. These hand crafted methods rely on domain
knowledge to construct. We would like to learn the optimal filters and features
based on training examples. In order to dynamically learn filters and features
we look to Convolutional Neural Networks (CNNs) which have shown their
dominance in computer vision. The power of CNNs is that they can learn image
filters which generate features for high accuracy classification.

Computer Vision and Pattern Recognition