Automated Processing of Sea Surface Images for the Determination of Whitecap Coverage
Sea surface images have been collected to determine the percentage whitecap coverage (W) since the late 1960s. Image processing methods have changed dramatically since the beginning of whitecap studies. An automated whitecap extraction (AWE) technique has been developed at the National University of...
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Published in | Journal of atmospheric and oceanic technology Vol. 26; no. 2; pp. 383 - 394 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Boston
American Meteorological Society
01.02.2009
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Subjects | |
Online Access | Get full text |
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Summary: | Sea surface images have been collected to determine the percentage whitecap coverage (W) since the late 1960s. Image processing methods have changed dramatically since the beginning of whitecap studies. An automated whitecap extraction (AWE) technique has been developed at the National University of Ireland, Galway, that allows images to be analyzed for percentage whitecap coverage without the need of a human analyst. AWE analyzes digital images and determines a suitable threshold with which whitecaps can be separated from unbroken background water. By determining a threshold for each individual image, AWE is suitable for images obtained in conditions of changing ambient illumination. AWE is also suitable to process images that have been taken from both stable and nonstable platforms (such as towers and research vessels, respectively). Using techniques based on derivative analysis, AWE provides an objective method to determine an appropriate threshold for the identification of whitecaps in sea surface images without the need for a human analyst. The automated method allows large numbers of images to be analyzed in a relatively short amount of time. AWE can be used to analyze hundreds of images per individual W data point, which produces more convergent values of W. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
ISSN: | 0739-0572 1520-0426 |
DOI: | 10.1175/2008JTECHO634.1 |