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Automated cloud screening of AVHRR imagery using split-and-merge clusteringPrevious methods to segment clouds from ocean in AVHRR imagery have shown varying degrees of success, with nighttime approaches being the most limited. An improved method of automatic image segmentation, the principal component transformation split-and-merge clustering (PCTSMC) algorithm, is presented and applied to cloud screening of both nighttime and daytime AVHRR data. The method combines spectral differencing, the principal component transformation, and split-and-merge clustering to sample objectively the natural classes in the data. This segmentation method is then augmented by supervised classification techniques to screen clouds from the imagery. Comparisons with other nighttime methods demonstrate its improved capability in this application. The sensitivity of the method to clustering parameters is presented; the results show that the method is insensitive to the split-and-merge thresholds.
Document ID
19920035262
Document Type
Reprint (Version printed in journal)
Authors
Gallaudet, Timothy C. (Scripps Institution of Oceanography La Jolla, CA, United States)
Simpson, James J. (Scripps Institution of Oceanography La Jolla, CA, United States)
Date Acquired
August 15, 2013
Publication Date
November 1, 1991
Publication Information
Publication: Remote Sensing of Environment
Volume: 38
ISSN: 0034-4257
Subject Category
METEOROLOGY AND CLIMATOLOGY
Distribution Limits
Public
Copyright
Other