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Fuzzy Object-based Generation of Cloud Motion from Sequences of Meteosat Satellite Imagery

Fuzzy Object-based Generation of Cloud Motion from Sequences of Meteosat Satellite Imagery
Fuzzy Object-based Generation of Cloud Motion from Sequences of Meteosat Satellite Imagery
Current approaches for generating wind data from cloud motion in time series of satellite imagery using maximum cross-correlation (MCC) techniques are proving increasingly inadequate for the atmospheric models used today, to the point where satellite-derived wind data are, in some cases, not used. An object-based cloud motion analysis technique has been developed to improve upon MCC alone. The approach uses a variety of motion analyses to identify the different components of cloud motion (e.g. motion due to wind, frontal motion, cloud growth/decay etc.). Each analysis is appropriate for a different aspect of cloud dynamics (internal texture motion, edge tracking, shape change etc.). The analyses produce vector fields that represent differently coupled mixtures of the constituent components of cloud motion. By determining the degree of coupling between each vector field, more complete knowledge of the cloud motion and its causes is obtained. Fuzzy logic is used to extract homogeneous cloud objects, and is also used to provide spatiotemporal smoothing of vectors. A method for determining the degree of coupling between the different vector fields and interpreting this in terms of the constituent motion types using fuzzy logic is also introduced.
Newland, F.T.
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Tatnall, A.R.L.
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Brown, M.
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Newland, F.T.
e1b8f8f6-f064-4cd9-bdab-abdb38fe3054
Tatnall, A.R.L.
8b3b9a71-2bc4-459d-8af2-67feb6b984fe
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049

Newland, F.T., Tatnall, A.R.L. and Brown, M. (1998) Fuzzy Object-based Generation of Cloud Motion from Sequences of Meteosat Satellite Imagery. First American Meteorological Society Meeting on Artificial Intelligence.

Record type: Conference or Workshop Item (Other)

Abstract

Current approaches for generating wind data from cloud motion in time series of satellite imagery using maximum cross-correlation (MCC) techniques are proving increasingly inadequate for the atmospheric models used today, to the point where satellite-derived wind data are, in some cases, not used. An object-based cloud motion analysis technique has been developed to improve upon MCC alone. The approach uses a variety of motion analyses to identify the different components of cloud motion (e.g. motion due to wind, frontal motion, cloud growth/decay etc.). Each analysis is appropriate for a different aspect of cloud dynamics (internal texture motion, edge tracking, shape change etc.). The analyses produce vector fields that represent differently coupled mixtures of the constituent components of cloud motion. By determining the degree of coupling between each vector field, more complete knowledge of the cloud motion and its causes is obtained. Fuzzy logic is used to extract homogeneous cloud objects, and is also used to provide spatiotemporal smoothing of vectors. A method for determining the degree of coupling between the different vector fields and interpreting this in terms of the constituent motion types using fuzzy logic is also introduced.

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More information

Published date: 1998
Additional Information: to be published Address: Phoenix, Arizona
Venue - Dates: First American Meteorological Society Meeting on Artificial Intelligence, 1998-01-01
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 250016
URI: https://eprints.soton.ac.uk/id/eprint/250016
PURE UUID: 75ff0258-070d-4b3f-9c3c-b371347da88d

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Date deposited: 04 May 1999
Last modified: 09 Nov 2018 17:31

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