Cloud motion analysis using multichannel correlation-relaxation labeling

Adrian N Evans

doi:10.1109/LGRS.2006.873343

Cloud motion analysis using multichannel correlation-relaxation labeling

Adrian N Evans

Department of Electronic & Electrical Engineering

Research output: Contribution to journal › Article › peer-review

27 Citations (SciVal)

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Abstract

Cloud motion vectors derived from sequences of remotely sensed data are widely used by numerical weather prediction models and other meteorological and climatic applications. One approach to computing cloud motion vectors is the correlation-relaxation labeling technique, in which a set of candidate vectors for each template is refined using relaxation labeling to provide a local smoothness constraint. In this letter, an extension of the correlation-relaxation labeling framework to tracking clouds in multichannel imagery is presented. As this multichannel approach takes advantage of the diversity between channels, it has the potential for producing motion vectors with a superior quality and coverage than can be achieved by any individual channel. Results for visible and infrared images from Meteostat Second Generation confirm the benefits of the multichannel approach.

Original language	English
Pages (from-to)	392-396
Number of pages	5
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	3
Issue number	3
DOIs	https://doi.org/10.1109/LGRS.2006.873343
Publication status	Published - Jul 2006

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title = "Cloud motion analysis using multichannel correlation-relaxation labeling",

abstract = "Cloud motion vectors derived from sequences of remotely sensed data are widely used by numerical weather prediction models and other meteorological and climatic applications. One approach to computing cloud motion vectors is the correlation-relaxation labeling technique, in which a set of candidate vectors for each template is refined using relaxation labeling to provide a local smoothness constraint. In this letter, an extension of the correlation-relaxation labeling framework to tracking clouds in multichannel imagery is presented. As this multichannel approach takes advantage of the diversity between channels, it has the potential for producing motion vectors with a superior quality and coverage than can be achieved by any individual channel. Results for visible and infrared images from Meteostat Second Generation confirm the benefits of the multichannel approach.",

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AB - Cloud motion vectors derived from sequences of remotely sensed data are widely used by numerical weather prediction models and other meteorological and climatic applications. One approach to computing cloud motion vectors is the correlation-relaxation labeling technique, in which a set of candidate vectors for each template is refined using relaxation labeling to provide a local smoothness constraint. In this letter, an extension of the correlation-relaxation labeling framework to tracking clouds in multichannel imagery is presented. As this multichannel approach takes advantage of the diversity between channels, it has the potential for producing motion vectors with a superior quality and coverage than can be achieved by any individual channel. Results for visible and infrared images from Meteostat Second Generation confirm the benefits of the multichannel approach.

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DO - 10.1109/LGRS.2006.873343

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JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

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Cloud motion analysis using multichannel correlation-relaxation labeling

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