| Title: |
Deep Learning for Ship Classification on Medium Resolution SAR Imagery |
| Authors: |
Moujahid, Bou, Laouz; Rodolphe, Vadaine; Guillaume, Hajduch; Fablet, Ronan |
| Contributors: |
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 Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Bretagne Sud (UBS)-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); 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 EPE)-Centre Inria de l'Université de Rennes; 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) |
| 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 |
| Publisher Information: |
CCSD |
| Publication Year: |
2023 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
Deep Learning; AIS; Medium resolution; Ship Classification; Sentinel-1; SAR Synthetic Aperture Radar; [SDE]Environmental Sciences; [MATH]Mathematics [math]; [PHYS]Physics [physics] |
| Subject Geographic: |
longyearbyen; Norway |
| 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%. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-04277648; https://hal.science/hal-04277648v2/document; https://hal.science/hal-04277648v2/file/Ship_classification_v12%20%281%29.pdf |
| Rights: |
https://creativecommons.org/publicdomain/zero/1.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.FE2AB6F0 |
| Database: |
BASE |