Publication Date
2018
Document Type
Thesis
Committee Members
Thomas Wischgoll (Advisor); John Gallagher (Committee Member); Michael Raymer (Committee Member); Barry Milligan (Other)
Degree Name
Master of Science (MS)
Abstract
Tracking of ships in satellite imagery is a challenging problem in remote sensing since it requires both object detection and object recognition. Most of the resources available only cover one of these problems and are often filled with machine learning techniques which are costly to train. Additionally, the techniques covered in these resources are often difficult to replicate or may be hard to combine with other solutions to get a full tracking algorithm. The proposed framework offers a transparent and efficient alternative to machine learning approaches and includes preprocessing, detection, and recognition needed for tracking. All components of the framework were created based on open source libraries to provide a transparent solution which can be easily modified for specific use cases.
Page Count
47
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2018
Copyright
Copyright 2018, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
