Deep Learning for Ship Classification on Medium Resolution SAR Imagery
International audience This research delves into the classification of maritime vessels, utilizing medium-resolution Synthetic Aperture Radar (SAR) imagery obtained from Sentinel-1, alongside Automatic Identification System (AIS) data streams. The investigation is specifically designed to address a...
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ftimtatlantique:oai:HAL:hal-04277648v2 2024-05-19T07:43:44+00:00 Deep Learning for Ship Classification on Medium Resolution SAR Imagery Moujahid, Bou, Laouz Rodolphe, Vadaine Guillaume, Hajduch Fablet, Ronan Equipe Observations Signal & Environnement (Lab-STICC_OSE) Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC) École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT) Département Mathematical and Electrical Engineering (IMT Atlantique - MEE) IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) Collecte Localisation Satellites (CLS) Océan Dynamique Observations Analyse (ODYSSEY) Université de Rennes (UR)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IMT Atlantique (IMT Atlantique) This work was performed under a research contract betweenCLS and IMT Atlantique. Part of the work was foundedsupported by ”France Relance”. We used Sentinel-1 dataacquired between 2017 and 2022 as part of the CopernicusSentinel programme. European Space Agency (ESA) longyearbyen, Norway 2023-05-02 https://hal.science/hal-04277648 https://hal.science/hal-04277648v2/document https://hal.science/hal-04277648v2/file/Ship_classification_v12%20%281%29.pdf en eng HAL CCSD hal-04277648 https://hal.science/hal-04277648 https://hal.science/hal-04277648v2/document https://hal.science/hal-04277648v2/file/Ship_classification_v12%20%281%29.pdf http://hal.archives-ouvertes.fr/licences/publicDomain/ info:eu-repo/semantics/OpenAccess SeaSAR 2023 - workshop on Coastal and Marine applications of SAR https://hal.science/hal-04277648 SeaSAR 2023 - workshop on Coastal and Marine applications of SAR, European Space Agency (ESA), May 2023, longyearbyen, Norway. pp.1-3 SAR Synthetic Aperture Radar Sentinel-1 Ship Classification Medium resolution AIS Deep Learning [SDE]Environmental Sciences [MATH]Mathematics [math] [PHYS]Physics [physics] info:eu-repo/semantics/conferenceObject Conference papers 2023 ftimtatlantique 2024-04-26T00:08:24Z International audience This research delves into the classification of maritime vessels, utilizing medium-resolution Synthetic Aperture Radar (SAR) imagery obtained from Sentinel-1, alongside Automatic Identification System (AIS) data streams. The investigation is specifically designed to address a ternary classification challenge involving three distinct ship categories: Tanker, Cargo, and Others. Leveraging a dataset comprising over 80,000 ship images, a Convolutional Neural Network (CNN) ensemble is applied. The results reveal a total classification accuracy of 79%. Conference Object Longyearbyen Archives ouvertes Hal IMT Atlantique |
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Open Polar |
collection |
Archives ouvertes Hal IMT Atlantique |
op_collection_id |
ftimtatlantique |
language |
English |
topic |
SAR Synthetic Aperture Radar Sentinel-1 Ship Classification Medium resolution AIS Deep Learning [SDE]Environmental Sciences [MATH]Mathematics [math] [PHYS]Physics [physics] |
spellingShingle |
SAR Synthetic Aperture Radar Sentinel-1 Ship Classification Medium resolution AIS Deep Learning [SDE]Environmental Sciences [MATH]Mathematics [math] [PHYS]Physics [physics] Moujahid, Bou, Laouz Rodolphe, Vadaine Guillaume, Hajduch Fablet, Ronan Deep Learning for Ship Classification on Medium Resolution SAR Imagery |
topic_facet |
SAR Synthetic Aperture Radar Sentinel-1 Ship Classification Medium resolution AIS Deep Learning [SDE]Environmental Sciences [MATH]Mathematics [math] [PHYS]Physics [physics] |
description |
International audience This research delves into the classification of maritime vessels, utilizing medium-resolution Synthetic Aperture Radar (SAR) imagery obtained from Sentinel-1, alongside Automatic Identification System (AIS) data streams. The investigation is specifically designed to address a ternary classification challenge involving three distinct ship categories: Tanker, Cargo, and Others. Leveraging a dataset comprising over 80,000 ship images, a Convolutional Neural Network (CNN) ensemble is applied. The results reveal a total classification accuracy of 79%. |
author2 |
Equipe Observations Signal & Environnement (Lab-STICC_OSE) Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC) École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT) Département Mathematical and Electrical Engineering (IMT Atlantique - MEE) IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) Collecte Localisation Satellites (CLS) Océan Dynamique Observations Analyse (ODYSSEY) Université de Rennes (UR)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IMT Atlantique (IMT Atlantique) This work was performed under a research contract betweenCLS and IMT Atlantique. Part of the work was foundedsupported by ”France Relance”. We used Sentinel-1 dataacquired between 2017 and 2022 as part of the CopernicusSentinel programme. European Space Agency (ESA) |
format |
Conference Object |
author |
Moujahid, Bou, Laouz Rodolphe, Vadaine Guillaume, Hajduch Fablet, Ronan |
author_facet |
Moujahid, Bou, Laouz Rodolphe, Vadaine Guillaume, Hajduch Fablet, Ronan |
author_sort |
Moujahid, Bou, Laouz |
title |
Deep Learning for Ship Classification on Medium Resolution SAR Imagery |
title_short |
Deep Learning for Ship Classification on Medium Resolution SAR Imagery |
title_full |
Deep Learning for Ship Classification on Medium Resolution SAR Imagery |
title_fullStr |
Deep Learning for Ship Classification on Medium Resolution SAR Imagery |
title_full_unstemmed |
Deep Learning for Ship Classification on Medium Resolution SAR Imagery |
title_sort |
deep learning for ship classification on medium resolution sar imagery |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04277648 https://hal.science/hal-04277648v2/document https://hal.science/hal-04277648v2/file/Ship_classification_v12%20%281%29.pdf |
op_coverage |
longyearbyen, Norway |
genre |
Longyearbyen |
genre_facet |
Longyearbyen |
op_source |
SeaSAR 2023 - workshop on Coastal and Marine applications of SAR https://hal.science/hal-04277648 SeaSAR 2023 - workshop on Coastal and Marine applications of SAR, European Space Agency (ESA), May 2023, longyearbyen, Norway. pp.1-3 |
op_relation |
hal-04277648 https://hal.science/hal-04277648 https://hal.science/hal-04277648v2/document https://hal.science/hal-04277648v2/file/Ship_classification_v12%20%281%29.pdf |
op_rights |
http://hal.archives-ouvertes.fr/licences/publicDomain/ info:eu-repo/semantics/OpenAccess |
_version_ |
1799483488345260032 |