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

D'Amore, M. and Remi, Le Scaon and Helbert, Jörn and Maturilli, Alessandro and Palomba, E. and Longobardo, A and Hiesinger, H. (2016) Machine Learning Applied to Dawn/VIR data of Vesta in view of MERTIS/BepiColombo. In: AGU Fall Meeting. AGU. 2016 AGU Fall Meeting, 12 - 16 December 2016, San Francisco, CA, USA.

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Official URL: https://agu.confex.com/agu/fm16/meetingapp.cgi/Paper/140789

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.

Item URL in elib:https://elib.dlr.de/108633/
Document Type:Conference or Workshop Item (Poster)
Title:Machine Learning Applied to Dawn/VIR data of Vesta in view of MERTIS/BepiColombo
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
D'Amore, M.mario.damore (at) dlr.deUNSPECIFIED
Remi, Le ScaonÉcole Polytechnique. Route de Saclay, 91128 Palaiseau, FranceUNSPECIFIED
Helbert, JörnJoern.Helbert (at) dlr.deUNSPECIFIED
Maturilli, AlessandroAlessandro.Maturilli (at) dlr.deUNSPECIFIED
Palomba, E.institute for interplanetary space physics - inaf, rome, italyUNSPECIFIED
Longobardo, Ainaf-laps, via del fosso del cavaliere 100, i-00133 rome, italyUNSPECIFIED
Hiesinger, H.westfälische wilhelms-universität münsterUNSPECIFIED
Date:2016
Journal or Publication Title:AGU Fall Meeting
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Publisher:AGU
Series Name:AGU Fall Meeting
Status:Published
Keywords:DAWN, machinelearning , Vesta
Event Title:2016 AGU Fall Meeting
Event Location:San Francisco, CA, USA
Event Type:international Conference
Event Dates:12 - 16 December 2016
Organizer:AGU
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Science and Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Erforschung des Weltraums
DLR - Research theme (Project):R - Projekt DAWN
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research > Leitungsbereich PF
Deposited By: Amore, Dr. Mario
Deposited On:15 Dec 2016 08:02
Last Modified:31 Jul 2019 20:05

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