Longman, F.S., Mihaylova, L. and Yang, L. (2018) A Gaussian Process Regression Approach for Fusion of Remote Sensing Images for Oil Spill Segmentation. In: Proceedings of the 21st IEEE International Conference on Information Fusion. 21st International Conference on Information Fusion, 10-13 Jul 2018, Cambridge, UK. IEEE ISBN 978-0-9964527-6-2
Abstract
Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. This paper presents a framework using Gaussian process (GP) to fuse SAR images of different modalities and to segment dark areas (assumed oil spill) for oil spill detection. A new covariance function; a product of an intrinsically sparse kernel and a Rational Quadratic Kernel (RQK) is used to model the prior of the estimated image allowing information to be transferred. The accuracy performance evaluation demonstrates that the proposed framework has 37% less RMSE per pixel and a compelling enhancement visually when compared with existing methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
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: | 11 Jun 2018 08:42 |
Last Modified: | 19 Dec 2022 13:49 |
Published Version: | https://doi.org/10.23919/ICIF.2018.8455304 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/ICIF.2018.8455304 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:131616 |