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Neural network approaches versus statistical methods in classification of multisource remote sensing dataNeural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.
Document ID
19900062611
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Benediktsson, Jon A.
(Purdue Univ. West Lafayette, IN, United States)
Swain, Philip H.
(Purdue Univ. West Lafayette, IN, United States)
Ersoy, Okan K.
(Purdue University West Lafayette, IN, United States)
Date Acquired
August 14, 2013
Publication Date
July 1, 1990
Publication Information
Publication: Vancouver, Canada, July 10-14, 1989) IEEE Transactions on Geoscience and Remote Sensing
ISSN: 0196-2892
Subject Category
Cybernetics
Accession Number
90A49666
Funding Number(s)
CONTRACT_GRANT: NAGW-925
Distribution Limits
Public
Copyright
Other

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