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
(1973). 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%.