Applying Structural EM in Autonomous Planetary Exploration Missions using Hyperspectral Image Spectroscopy

In this paper, we use the Bayesian Structural EM algorithm as a classification method to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm presented combines the standard Expectation Maximisation (EM), which optimises parameters, with structure search for model selection. We use the Bayesian Information Criterion (BIC) score to learn the network struc ture. The procedure only converges to a local maxima, thus requiring a good initial graph structure. Two initial structures are used: the Naïve Bayes, and the Tree-Augmented-Naïve Bayes structures. Our preliminary experiments show that the former results in a structure that can correctly determine the presence and types of minerals with merely 13% accuracy while the latter results in a structure that has approximately 94% accuracy.