Machine Learning Applied to Dawn/VIR data of Vesta in view of MERTIS/BepiColombo

Abstract

Remote sensing spectroscopy is one of the most commonly used technique in planetary science and for recent instruments producing huge amount of data, classic methods could fails to unlock the full scientific potential buried in the measurements. We explored several Machine Learning techniques: multi-step clustering method is developed, using an image segmentation method, a stream algorithm, and hierarchical clustering. The MErcury Radiometer and Thermal infrared Imaging Spectrometer (MERTIS) is part of the payload of the Mercury Planetary Orbiter spacecraft of the ESA-JAXA BepiColombo mission. MERTIS’s scientific goals are to infer rock-forming minerals, to map surface composition, and to study surface temperature variations on Mercury. The NASA mission DAWN carry a suites of instruments aimed at understanding the two most massive objects in the main asteroid belt: Vesta and Ceres. DAWN has already successfully completed the exploration of Vesta in September 2012 and it is now in the last phase of the mission around Ceres. To cope with the stream of data that will be delivered by MERTIS, we developed an algorithm that could aggregate new data as they come in during the mission giving the scientist a guide for the most interesting and new discovery on Mercury. The DAWN/VESTA VIR data is a testbed for the algorithm. The algorithm identified the Olivine outcrops around two craters on Vesta’s surface described in Ammannito et al., 2013. We furthermore mimic the data acquisition process as if the mission were dumping the data live. The algorithm provides insightful information on the novelty and classes int he data as they are collected. This will enhance MERTIS targeting and maximize its scientific return during BepiColombo mission at Mercury. E Ammannito et al. “Olivine in an unexpected location on Vesta/’s surface”. In: Nature 504.7478 (2013), pp. 122–125.