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

QSpace/Manakin Repository

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

Show simple item record


dc.contributor.author Melgani, Farid en_US
dc.date.accessioned 2009-11-25T13:06:36Z
dc.date.available 2009-11-25T13:06:36Z
dc.date.issued 2001 en_US
dc.identifier.citation Engineering Journal of Qatar University, 2001, Vol. 14, Pages 77-104. en_US
dc.identifier.uri http://hdl.handle.net/10576/7990
dc.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
dc.language.iso en en_US
dc.publisher Qatar University en_US
dc.subject Engineering: Research Papers en_US
dc.title An Evaluation Of The Explicit Fuzzy Method Using Parametric And Non-Parametric Approaches For Supervised Classification Of Multispectral Remote Sensing Data en_US
dc.type Article en_US
dc.identifier.pagination 77-104 en_US
dc.identifier.volume 14 en_US

Files in this item

Files Size Format View
abstract.pdf 2.211Kb PDF View/Open
abstract.doc 20.5Kb Microsoft Word View/Open
06-01-14-0005-fulltext.pdf 2.547Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Search QSpace


Advanced Search

Browse

My Account