A neural network approach to cloud classification from multi-temporal satellite imagery
A neural network approach to cloud classification from multi-temporal satellite imagery
In response both to the general lack of automatic methods to analyse the increasing amount of satellite data, and to the availability of multi-temporal information at high temporal resolution (e.g. Meteosat, 30 minutes), a new artificial neural network (ANN) method for classifying clouds has been developed. A recently developed cloud tracking method, utilising a Hopfield neural network, is used to acquire new dynamic cloud parameters from satellite image sequences. These parameters are analysed, and their contribution towards accurate classification is discussed
0852966415
116-121
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Cote, S.
ffd99deb-a9e2-4469-9335-fce9b3f477a9
Tatnall, A.R.L.
2c9224b6-4faa-4bfd-9026-84e37fa6bdf3
1995
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Cote, S.
ffd99deb-a9e2-4469-9335-fce9b3f477a9
Tatnall, A.R.L.
2c9224b6-4faa-4bfd-9026-84e37fa6bdf3
Lewis, H.G., Cote, S. and Tatnall, A.R.L.
(1995)
A neural network approach to cloud classification from multi-temporal satellite imagery.
In Fourth International Conference on Artificial Neural Networks.
IEEE.
.
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Conference or Workshop Item
(Paper)
Abstract
In response both to the general lack of automatic methods to analyse the increasing amount of satellite data, and to the availability of multi-temporal information at high temporal resolution (e.g. Meteosat, 30 minutes), a new artificial neural network (ANN) method for classifying clouds has been developed. A recently developed cloud tracking method, utilising a Hopfield neural network, is used to acquire new dynamic cloud parameters from satellite image sequences. These parameters are analysed, and their contribution towards accurate classification is discussed
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Published date: 1995
Venue - Dates:
Fourth International Conference on Artificial Neural Networks, Cambridge, UK, 1995-06-26 - 1995-06-28
Identifiers
Local EPrints ID: 23742
URI: http://eprints.soton.ac.uk/id/eprint/23742
ISBN: 0852966415
PURE UUID: 839177a8-7164-4d3b-85c6-a6143661e5b6
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Date deposited: 14 Feb 2007
Last modified: 06 Mar 2024 02:36
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Author:
S. Cote
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