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The use of the Hopfield neural network to measure sea-surface velocities from satellite images

The use of the Hopfield neural network to measure sea-surface velocities from satellite images
The use of the Hopfield neural network to measure sea-surface velocities from satellite images
The knowledge of ocean surface circulation is of major importance for many applications, including the understanding of global climate, resources exploitation, and containment of chemical spills. In this letter, sea-surface feature tracking based on the Hopfield neural network (NN) is described. The method is based on the minimization of an energy function that represents the feature tracking problem. A Hopfield NN is used to merge cross-correlation information with prior knowledge of sea-surface flows and image contextual information. It has been tested on real satellite images. A set of five Advanced Very High Resolution Radiometer thermal images of the coastal zone of California, along with a data set of coincident surface drifters positions, was used to test the method. Results of the new analysis are compared within situ data and previous results using other techniques. The method can be used on various kinds of images for tracking and also find other applications in image registration and pattern recognition.
feature extraction, hopfield networks, neural networks (NNs), sea surface, tracking.
1545-598X
624-628
Cote, S.
ffd99deb-a9e2-4469-9335-fce9b3f477a9
Tatnall, A.R.L.
2c9224b6-4faa-4bfd-9026-84e37fa6bdf3
Cote, S.
ffd99deb-a9e2-4469-9335-fce9b3f477a9
Tatnall, A.R.L.
2c9224b6-4faa-4bfd-9026-84e37fa6bdf3

Cote, S. and Tatnall, A.R.L. (2007) The use of the Hopfield neural network to measure sea-surface velocities from satellite images. IEEE Geoscience and Remote Sensing Letters, 4 (4), 624-628. (doi:10.1109/LGRS.2007.900700).

Record type: Article

Abstract

The knowledge of ocean surface circulation is of major importance for many applications, including the understanding of global climate, resources exploitation, and containment of chemical spills. In this letter, sea-surface feature tracking based on the Hopfield neural network (NN) is described. The method is based on the minimization of an energy function that represents the feature tracking problem. A Hopfield NN is used to merge cross-correlation information with prior knowledge of sea-surface flows and image contextual information. It has been tested on real satellite images. A set of five Advanced Very High Resolution Radiometer thermal images of the coastal zone of California, along with a data set of coincident surface drifters positions, was used to test the method. Results of the new analysis are compared within situ data and previous results using other techniques. The method can be used on various kinds of images for tracking and also find other applications in image registration and pattern recognition.

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More information

Published date: October 2007
Additional Information: In this paper the artificial intelligence technique developed by the authors and used by a number of researchers for a range of different applications over the past ten years was applied to satellite ocean data and compared for the first time with the results achieved using other techniques. The favourable results which have been reported in a number of international conferences are discussed.
Keywords: feature extraction, hopfield networks, neural networks (NNs), sea surface, tracking.
Organisations: Astronautics Group

Identifiers

Local EPrints ID: 49356
URI: https://eprints.soton.ac.uk/id/eprint/49356
ISSN: 1545-598X
PURE UUID: b5c4dfd0-3d44-4feb-bade-4d9df03747e2

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Date deposited: 01 Nov 2007
Last modified: 13 Mar 2019 20:54

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Contributors

Author: S. Cote
Author: A.R.L. Tatnall

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