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Feature Selection for Classification of Polar Regions Using a Fuzzy Expert SystemLabeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.
Document ID
19970023200
Acquisition Source
Langley Research Center
Document Type
Reprint (Version printed in journal)
Authors
Penaloza, Mauel A.
(South Dakota School of Mines and Technology Rapid City, SD United States)
Welch, Ronald M.
(South Dakota School of Mines and Technology Rapid City, SD United States)
Date Acquired
August 17, 2013
Publication Date
January 1, 1996
Publication Information
Publication: Remote Sensing of the Environment
Publisher: Elsevier Science Inc.
Volume: 58
ISSN: 0034-4257
Subject Category
Environment Pollution
Report/Patent Number
NAS 1.26:204574
NASA-CR-204574
Accession Number
97N72176
Funding Number(s)
CONTRACT_GRANT: NAS1-19077
Distribution Limits
Public
Copyright
Public Use Permitted.
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