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Proper mapping of a planet's surrounding can offer in depth understanding about the geology of the surface and environmental conditions. The high cost of planetary rover missions limits risk-taking and as a result restricts scientific exploration. This constraint is further compounded by limited autonomy that requires time-consuming intervention of Earth-based operators to ensure safe operation in previously unexplored areas. The proposed autonomous classification system utilizes vision algorithms to gather textural information from the surface of rocks. The input is black and white images of hand samples taken in a controlled lighting environment. The classification is based on Haralick's textural feature extraction. Seven of the original 14 parameters introduced by Haralick (1973) are used: angular second moment, contrast, correlation, inverse difference moment, entropy, sum average, and sum of squares. Once the features are extracted, the system compares them against a catalogue of values from pre-processed rocks. Using Bayes' theorem, the system computes statistical probabilities of classifying the sample based on its former exposures. The system has been tested using 180 sample points from 30 rock samples, and has achieved classification accuracy of 80%. I would like to thank my supervisors Professor Alex Ellery and Professor Claire Samson for their guidance, support, and insightful suggestions. Alex's passion for space robotics and planetary exploration inspired me to pursue a career in this field. Claire's drive for perfection and surplus of skills is my motivation to want to expand my expertise.

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