Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images

In this paper, we describe an innovative content annotation method for high-resolution Synthetic Aperture Radar (SAR) images generating routinely user-defined semantic labels for sequences of small contiguous image patches, while the full surface areas of our images cover hundreds of km in width and...

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Bibliographic Details
Published in:2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Main Authors: Dumitru, Corneliu Octavian, Schwarz, Gottfried, Karmakar, Chandrabali, Datcu, Mihai
Format: Conference Object
Language:unknown
Published: 2021
Subjects:
EO Data Science
Sea ice
Online Access:https://elib.dlr.de/142804/
https://igarss2021.com/view_paper.php?PaperNum=3147
id ftdlr:oai:elib.dlr.de:142804
record_format openpolar
spelling ftdlr:oai:elib.dlr.de:142804 2024-05-19T07:48:19+00:00 Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images Dumitru, Corneliu Octavian Schwarz, Gottfried Karmakar, Chandrabali Datcu, Mihai 2021-07 https://elib.dlr.de/142804/ https://igarss2021.com/view_paper.php?PaperNum=3147 unknown Dumitru, Corneliu Octavian und Schwarz, Gottfried und Karmakar, Chandrabali und Datcu, Mihai (2021) Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images. In: International Geoscience and Remote Sensing Symposium (IGARSS), Seiten 1-4. IGARSS 2021, 2021-07-11 - 2021-07-16, Brussels, Belgium. doi:10.1109/igarss47720.2021.9553334 <https://doi.org/10.1109/igarss47720.2021.9553334>. EO Data Science Konferenzbeitrag PeerReviewed 2021 ftdlr https://doi.org/10.1109/igarss47720.2021.9553334 2024-04-25T00:56:38Z In this paper, we describe an innovative content annotation method for high-resolution Synthetic Aperture Radar (SAR) images generating routinely user-defined semantic labels for sequences of small contiguous image patches, while the full surface areas of our images cover hundreds of km in width and length. Based on this method, we are able to generate a sea-ice dataset that is used in projects to validate the developed machine learning methods. Conference Object Sea ice German Aerospace Center: elib - DLR electronic library 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 4268 4271
institution Open Polar
collection German Aerospace Center: elib - DLR electronic library
op_collection_id ftdlr
language unknown
topic EO Data Science
spellingShingle EO Data Science
Dumitru, Corneliu Octavian
Schwarz, Gottfried
Karmakar, Chandrabali
Datcu, Mihai
Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images
topic_facet EO Data Science
description In this paper, we describe an innovative content annotation method for high-resolution Synthetic Aperture Radar (SAR) images generating routinely user-defined semantic labels for sequences of small contiguous image patches, while the full surface areas of our images cover hundreds of km in width and length. Based on this method, we are able to generate a sea-ice dataset that is used in projects to validate the developed machine learning methods.
format Conference Object
author Dumitru, Corneliu Octavian
Schwarz, Gottfried
Karmakar, Chandrabali
Datcu, Mihai
author_facet Dumitru, Corneliu Octavian
Schwarz, Gottfried
Karmakar, Chandrabali
Datcu, Mihai
author_sort Dumitru, Corneliu Octavian
title Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images
title_short Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images
title_full Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images
title_fullStr Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images
title_full_unstemmed Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images
title_sort machine learning-based paradigm for boosting the semantic annotation of eo images
publishDate 2021
url https://elib.dlr.de/142804/
https://igarss2021.com/view_paper.php?PaperNum=3147
genre Sea ice
genre_facet Sea ice
op_relation Dumitru, Corneliu Octavian und Schwarz, Gottfried und Karmakar, Chandrabali und Datcu, Mihai (2021) Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images. In: International Geoscience and Remote Sensing Symposium (IGARSS), Seiten 1-4. IGARSS 2021, 2021-07-11 - 2021-07-16, Brussels, Belgium. doi:10.1109/igarss47720.2021.9553334 <https://doi.org/10.1109/igarss47720.2021.9553334>.
op_doi https://doi.org/10.1109/igarss47720.2021.9553334
container_title 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
container_start_page 4268
op_container_end_page 4271
_version_ 1799488868015144960

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