Machine Learning and Data Fusion of Simulated Remote Sensing Data

Modeling and simulation tools are described and implemented in a single workflow to develop a means of simulating a ship wake followed by simulated synthetic aperture radar (SAR) and infra-red (IR) images of these ship wakes. A parametric study across several different ocean environments and simulated remote sensing platforms is conducted to generate a preliminary data set that is used for training and testing neural network--based ship wake detection models. Several different model architectures are trained and tested, which are able to provide a high degree of accuracy in classifying whether input SAR images contain a persistent ship wake. Several data fusion models are explored to understand how fusing data from different SAR bands may improve ship wake detection, with some combinations of neural networks and data fusion models achieving perfect or near-perfect performance. Finally, an outline for a future study into multi-physics data fusion across multiple sensor modalities is created and discussed.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Aerospace Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Higgins, Erik Tracy

Chairs dc:contributor.committeechair

• Paterson, Eric G.
• Freeman, Laura J.

Committee members dc:contributor.committeemember

• Pitt, Jonathan
• Xiao, Heng
• England, Scott L.

• ship wake
• naval hydrodynamics
• remote sensing
• neural networks
• data fusion

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37294

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115895