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Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data

Picon, Artzai, Ghita, Ovidiu, Whelan, Paul F. orcid logoORCID: 0000-0001-9230-7656 and Iriondo, Pedro M. (2009) Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data. IEEE Transactions on Industrial Informatics, 5 (4). pp. 483-494. ISSN 1551-3203

Hyperspectral 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.
Item Type:Article (Published)
Uncontrolled Keywords:image analysis; WEEE Directive; environmental science computing; fuzzy set theory; image classification; industrial waste;
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Publisher:Institute of Electrical and Electronics Engineers
Official URL:http://dx.doi.org/10.1109/TII.2009.2031238
Copyright Information:©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:15600
Deposited On:04 Aug 2010 12:57 by DORAS Administrator . Last Modified 11 Jan 2019 15:58

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