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A neural network approach to cloud classificationIt is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery achieves an overall cloud identification accuracy of 93 percent. Cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96 percent, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92 percent, cumulus at 90 percent. The use of the neural network does not improve cirrus classification accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. While most cloud classification algorithms rely on linear parametric schemes, the present study is based on a nonlinear, nonparametric four-layer neural network approach. A three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure are compared. A significant finding is that significantly higher accuracies are attained with the nonparametric approaches using only 20 percent of the database as training data, compared to 67 percent of the database in the linear approach.
Document ID
19910030211
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Lee, Jonathan
(South Dakota School of Mines and Technology Rapid City, SD, United States)
Weger, Ronald C.
(South Dakota School of Mines and Technology Rapid City, SD, United States)
Sengupta, Sailes K.
(South Dakota School of Mines and Technology Rapid City, SD, United States)
Welch, Ronald M.
(South Dakota School of Mines and Technology Rapid City, United States)
Date Acquired
August 15, 2013
Publication Date
September 1, 1990
Publication Information
Publication: IEEE Transactions on Geoscience and Remote Sensing
Volume: 28
ISSN: 0196-2892
Subject Category
Meteorology And Climatology
Accession Number
91A14834
Funding Number(s)
CONTRACT_GRANT: NAG1-542
CONTRACT_GRANT: NSF ATM-88-16052
Distribution Limits
Public
Copyright
Other

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