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Machine Learning for Sea Ice Monitoring from Satellites

Dumitru, Corneliu Octavian and Andrei, Vlad and Schwarz, Gottfried and Datcu, Mihai (2019) Machine Learning for Sea Ice Monitoring from Satellites. Munich Remote Sensing Symposium 2019, 18.-20. Sept. 2019, Munich, Germany.

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Official URL: http://www.pf.bgu.tum.de/isprs/mrss19/

Abstract

Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. To this end, we applied modern machine learning techniques to produce ice-describing semantic maps of the polar regions of the Earth. Time series of these maps can then be exploited for local and regional change maps of selected areas. What we expect, however, are fully-automated unsupervised routine classifications of sea ice regions that are needed for the rapid and reliable monitoring of shipping routes, drifting and disintegrating icebergs, snowfall and melting on ice, and other dynamic climate change indicators. Therefore, we designed and implemented an automated processing chain that analyses and interprets the specific ice-related content of high-resolution synthetic aperture radar (SAR) images. We trained this system with selected images covering various use cases allowing us to interpret these images with modern machine learning approaches. In the following, we describe a system comprising representation learning, variational inference, and auto-encoders. Test runs have already demonstrated its usefulness and stability that can pave the way towards future artificial intelligence systems extending, for instance, the current capabilities of traditional image analysis by including content-related image understanding.

Item URL in elib:https://elib.dlr.de/130273/
Document Type:Conference or Workshop Item (Poster)
Title:Machine Learning for Sea Ice Monitoring from Satellites
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Dumitru, Corneliu OctavianCorneliu.Dumitru (at) dlr.deUNSPECIFIED
Andrei, VladTUMUNSPECIFIED
Schwarz, GottfriedGottfried.Schwarz (at) dlr.deUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:2019
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Machine Learning, Sea Ice Monitoring
Event Title:Munich Remote Sensing Symposium 2019
Event Location:Munich, Germany
Event Type:international Conference
Event Dates:18.-20. Sept. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Karmakar, Chandrabali
Deposited On:28 Nov 2019 08:33
Last Modified:28 Nov 2019 08:33

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