Longman, F.S., Mihaylova, L.S. orcid.org/0000-0001-5856-2223 and Coca, D. (2017) Oil Spill Segmentation in Fused Synthetic Aperture Radar Images. In: 2016 4th International Conference on Control Engineering & Information Technology (CEIT). 4th International Conference on Control Engineering & Information Technology, 16-18 Dec 2016, Hammamet, Tunisia. IEEE ISBN 978-1-5090-1055-4
Abstract
Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. Systems such as the Envisat and RADARSAT have been used independently in many studies to detect oil spill. This paper presents an automatic feature based image registration and fusion algorithm for oil spill monitoring using SAR images. A range of metrics are used to evaluate the performance of the algorithm and to demonstrate the benefits of fusing SAR images of different modalities. The proposed framework has shown 45% improvement of the oil spill location when compared with the individual images before the fusion
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE.This is an author produced version of a paper subsequently published in Control Engineering & Information Technology (CEIT), 2016 4th International Conference on. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Oil Spill; Synthetic Aperture Radar (SAR); Registration; Image Fusion; Segmentation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Jan 2017 10:14 |
Last Modified: | 18 Jul 2017 05:20 |
Published Version: | https://doi.org/10.1109/CEIT.2016.7929055 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/CEIT.2016.7929055 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109886 |