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dc.contributor.authorPicon, Artzai
dc.contributor.authorGhita, Ovidiu
dc.contributor.authorWhelan, PF
dc.contributor.authorIriondo, Pedro M.
dc.date.accessioned2016-04-19T10:01:41Z
dc.date.available2016-04-19T10:01:41Z
dc.date.issued2009
dc.identifier.citationIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS Volume: 5 Issue: 4 Pages: 483-494en
dc.identifier.issn1551-3203en
dc.identifier.urihttp://hdl.handle.net/11556/198
dc.description.abstractHyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (FUzzy Spectral and Spatial classifiER) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification.en
dc.description.sponsorshipEtortek Program of the Basque Government, Dublin City University TII-09-04-0057.R1en
dc.language.isoengen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855 USAen
dc.titleFuzzy Spectral and Spatial Feature Integration for Classification of Nonferrous Materials in Hyperspectral Dataen
dc.typearticleen
dc.identifier.doi10.1109/TII.2009.2031238en
dc.isiYesen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsHyperspectral image processingen
dc.subject.keywordsimage classificationen
dc.subject.keywordsspectral fuzzy setsen


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