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Cloud motion analysis using multichannel correlation-relaxation labeling


Reference:

Evans, A. N., 2006. Cloud motion analysis using multichannel correlation-relaxation labeling. IEEE Geoscience and Remote Sensing Letters, 3 (3), pp. 392-396.

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    Official URL:

    http://dx.doi.org/10.1109/LGRS.2006.873343

    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.

    Details

    Item Type Articles
    CreatorsEvans, A. N.
    DOI10.1109/LGRS.2006.873343
    DepartmentsFaculty of Engineering & Design > Electronic & Electrical Engineering
    Publisher StatementIEEEGRSLJul2006.pdf: ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    RefereedYes
    StatusPublished
    ID Code14914
    Additional Informationatmospheric techniques, clouds, remote sensing, Meteostat, Second Generation, cloud motion analysis, cloud tracking, infrared images, multichannel correlation-relaxation, labeling multichannel imagery, numerical weather prediction model, visible images, motion analysis, multichannel images

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