Dumitru, Corneliu Octavian and Andrei, Vlad and Schwarz, Gottfried and Datcu, Mihai (2019) Machine Learning for Sea Ice Monitoring from Satellites. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. Munich Remote Sensing Symposium 2019, 2019-09-18 - 2019-09-20, Munich, Germany. doi: 10.5194/isprs-archives-XLII-2-W16-83-2019. ISSN 1682-1750.
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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/ | ||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Poster) | ||||||||||||||||||||
Title: | Machine Learning for Sea Ice Monitoring from Satellites | ||||||||||||||||||||
Authors: |
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Date: | 2019 | ||||||||||||||||||||
Journal or Publication Title: | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | ||||||||||||||||||||
Refereed publication: | No | ||||||||||||||||||||
Open Access: | Yes | ||||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||||
In SCOPUS: | Yes | ||||||||||||||||||||
In ISI Web of Science: | No | ||||||||||||||||||||
DOI: | 10.5194/isprs-archives-XLII-2-W16-83-2019 | ||||||||||||||||||||
ISSN: | 1682-1750 | ||||||||||||||||||||
Status: | Published | ||||||||||||||||||||
Keywords: | Machine Learning, Sea Ice Monitoring | ||||||||||||||||||||
Event Title: | Munich Remote Sensing Symposium 2019 | ||||||||||||||||||||
Event Location: | Munich, Germany | ||||||||||||||||||||
Event Type: | international Conference | ||||||||||||||||||||
Event Start Date: | 18 September 2019 | ||||||||||||||||||||
Event End Date: | 20 September 2019 | ||||||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||
HGF - Program: | Space | ||||||||||||||||||||
HGF - Program Themes: | Earth Observation | ||||||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||||||
DLR - Program: | R EO - Earth Observation | ||||||||||||||||||||
DLR - Research theme (Project): | R - Vorhaben hochauflösende Fernerkundungsverfahren (old) | ||||||||||||||||||||
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: | 24 Apr 2024 20:33 |
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