An Evaluation Of The Explicit Fuzzy Method Using Parametric And Non-Parametric Approaches For Supervised Classification Of Multispectral Remote Sensing Data

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contributor.author Melgani, Farid en_US
contributor.author Al-Hashemy, Bakir A. R. en_US
contributor.author Taha, Saleem M. R. en_US
date.accessioned 2009-11-25T13:06:36Z en_US
date.available 2009-11-25T13:06:36Z en_US
date.issued 2001 en_US
identifier.citation Engineering Journal of Qatar University, 2001, Vol. 14, Pages 77-104. en_US
identifier.uri http://hdl.handle.net/10576/7990 en_US
description.abstract Fuzzy Classification is of great interest because of its capacity to provide more useful information for Geographic Information Systems. This paper describes an Explicit Fuzzy Supervised Classification method, which consists of three steps. The explicit fuzzyfication is the first step where the pixel is transformed into a matrix of membership degrees representing the fuzzy inputs of the process. Then, in the second step, a MIN fuzzy reasoning rule followed by a rescaling operation are applied to deduce the fuzzy outputs, or in other words, the fuzzy classification of the pixel. Finally, a defuzzyfication step is carried out to produce a hard classification. The classification results ofLandsat TM data show the promising performance of the method and, particularly, the classification time. These results are compared with those produced by the Maximum Likelihood method and a non-parametric method based on the use of Artificial Neural Networks. en_US
language.iso en en_US
publisher Qatar University en_US
subject Engineering: Research Papers en_US
title An Evaluation Of The Explicit Fuzzy Method Using Parametric And Non-Parametric Approaches For Supervised Classification Of Multispectral Remote Sensing Data en_US
type Article en_US
identifier.pagination 77-104 en_US
identifier.volume 14 en_US


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