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An intercomparison of artificial intelligence approaches for polar scene identificationThe following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.
Document ID
19930048364
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Tovinkere, V. R.
(NASA Langley Research Center Hampton, VA, United States)
Penaloza, M.
(NASA Langley Research Center Hampton, VA, United States)
Logar, A.
(NASA Langley Research Center Hampton, VA, United States)
Lee, J.
(NASA Langley Research Center Hampton, VA, United States)
Weger, R. C.
(NASA Langley Research Center Hampton, VA, United States)
Berendes, T. A.
(NASA Langley Research Center Hampton, VA, United States)
Welch, R. M.
(South Dakota School of Mines and Technology Rapid City, United States)
Date Acquired
August 16, 2013
Publication Date
March 20, 1993
Publication Information
Publication: Journal of Geophysical Research
Volume: 98
Issue: D3
ISSN: 0148-0227
Subject Category
Meteorology And Climatology
Accession Number
93A32361
Funding Number(s)
CONTRACT_GRANT: NAS1-19077
Distribution Limits
Public
Copyright
Other

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