Classification of Mars Terrain Using Multiple Data Sources

Images of Mars are being collected faster than they can be analyzed by planetary scientists. Automatic analysis of images would enable more rapid and more consistent image interpretation and could draft geologic maps where none yet exist.

In this work we develop a method for incorporating images from multiple instruments to classify Martian terrain into multiple types. Each image is segmented into contiguous groups of similar pixels, called superpixels, with an associated vector of discriminative features. We have developed and tested several classification algorithms to associate a best class to each superpixel.