Autonomous rock classification using Bayesian image analysis for Rover-based planetary exploration
A robust classification system is proposed to support autonomous geological mapping of rocky outcrops using grayscale digital images acquired by a planetary exploration rover. The classifier uses 13 Haralick textural parameters to describe the surface of rock samples, automatically catalogues this information into a 5-bin data structure, computes Bayesian probabilities, and outputs an identification.The system has been demonstrated using a library of 30 digital images of igneous, sedimentary and metamorphic rocks. The images are 3.5×3.5cm<sup>2</sup> in size and composed of 256×256 pixels with 256 grayscale levels. They are first converted to gray level co-occurrence matrices which quantify the number of times adjacent pixels of similar intensity are present. The Haralick parameters are computed from these matrices. When all 13 parameters are used, classification accuracy, defined using an empirical scoring system, is 65% due to a large number of false positives. When the number of parameters and the choice of parameter is optimized, classification accuracy increases to 80%. The best results were achieved with 3 parameters that can be interpreted visually (angular second moment, contrast, correlation) together with two statistical parameters (sum of squares variance and difference variance) and a parameter derived from information theory (information measure of correlation II).The system has been kept simple not to draw excessive computational power from the rover. It could, however, be easily extended to handle additional parameters such as images acquired at different wavelengths.
|Keywords||Autonomous geology, Bayesian network, Exploration Rover, Haralick parameter, Planetary exploration, Textural analysis|
|Journal||Computers and Geosciences|
Sharif, H. (Helia), Ralchenko, M. (Maxim), Samson, C, & Ellery, A. (2015). Autonomous rock classification using Bayesian image analysis for Rover-based planetary exploration. Computers and Geosciences, 83, 153–167. doi:10.1016/j.cageo.2015.05.011