| Title: |
Automated Ship Detection and Characterization in Sentinel-2 Images: A Comprehensive Approach |
| Authors: |
Moujahid, Bou-Laouz; Rodolphe, Vadaine; Guillaume, Hajduch; Fablet, Ronan |
| Contributors: |
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); 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); 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) |
| Source: |
https://hal.science/hal-04359761 ; 2023. |
| Publisher Information: |
CCSD |
| Publication Year: |
2023 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
Deep Neural Network; Sentinel-2; Ship Detection; Ship Characterization; Faster R-CNN; Medium Resolution; Object Detection; [INFO]Computer Science [cs]; [PHYS]Physics [physics]; [STAT]Statistics [stat] |
| Description: |
The automatic detection and characterization of ships in optical remote sensing images is a key challenge for maritime surveillance applications. This paper presents an automated system specifically designed for ship detection in medium-resolution Sentinel-2 images. The proposed approach relies on a deep learning model trained on a dataset comprising over 6000 annotated Sentinel-2 images. It achieves a detection rate of 93%, with an average of 2.1 to 3.9 false alarms per Sentinel-2 image. Besides the detection task, it also addresses the estimation of ship lengths as well as ship headings. It yields a mean error of 15.36m ± 19.57m for ship lengths, and estimates ship headings with an accuracy of 93%. This contribution significanly enhances the performance of ship detection and characterization systems in optical remote sensing imagery. |
| Document Type: |
report |
| Language: |
English |
| Availability: |
https://hal.science/hal-04359761; https://hal.science/hal-04359761v1/document; https://hal.science/hal-04359761v1/file/paper_hal.pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.9EA44D39 |
| Database: |
BASE |