Master Thesis Online
Ship detection using SAR imagery and machine learning techniques
Remacle, Delphine 
Promotor(s) :
Van Messem, Arnout
;
Kirkove, Murielle
;
Barth, Alexander
Date of defense : 6-Sep-2023 • Permalink : http://hdl.handle.net/2268.2/18468
Memoire Remacle Delphine.pdfDetails
| Title : | Ship detection using SAR imagery and machine learning techniques |
| Translated title : | [fr] Détection de bateaux à l'aide d'images SAR et de techniques d'apprentissage automatique |
| Author : | Remacle, Delphine
|
| Date of defense : | 6-Sep-2023 |
| Advisor(s) : | Van Messem, Arnout
Kirkove, Murielle
Barth, Alexander
|
| Committee's member(s) : | Haesbroeck, Gentiane
Rigo, Michel
Schneiders, Jean-Pierre
Geurts, Pierre
|
| Language : | English |
| Number of pages : | 140 |
| Keywords : | [en] machine learning [en] computer vision [en] deep learning [en] ship detection [en] SAR imagery |
| Discipline(s) : | Physical, chemical, mathematical & earth Sciences > Mathematics |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en sciences mathématiques, à finalité approfondie |
| Faculty: | Master thesis of the Faculté des Sciences |
Abstract
[en] This master's thesis is part of a project carried out by several students, which goal is to find a suitable program of ship detection from Synthetic-Aperture Radar (SAR) imagery by using machine learning techniques. Here, we have tested the following deep learning techniques: YOLO versions 3 and 4, and Mask R-CNN.
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The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.