Mars terrain image classification using Cartesian genetic programming

Automatically classifying terrain such as rocks, sand and gravel from images is a challenging machine vision problem. In addition to human designed approaches, a great deal of progress has been made using machine learning techniques to perform classification from images. In this work, we demonstrate the first known use of <i>Cartesian Genetic Programming</i> (CGP) to this problem. Our CGP for Image Processing (CGP-IP) system quickly learns classifiers and detectors for certain terrain types. The learned program outperforms currently used techniques for classification tasks performed on a panorama image collected by the Mars Exploration Rover Spirit.