Support vector machines in hyperspectral imaging spectroscopy with application to material identification
Garcia-Allende, P.B.; Anabitarte, F.; Conde, O.M.; Mirapeix, J.; Madruga, F.J.; Lopez-Higuera, J.M. (2008). Support vector machines in hyperspectral imaging spectroscopy with application to material identification, in: Sylvia S. Shen, Paul E. Lewis Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XIV. Proceedings of SPIE, the International Society for Optical Engineering, 6966: pp. 1-7. https://dx.doi.org/10.1117/12.770306
In: Sylvia S. Shen, Paul E. Lewis (2008). Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XIV. Proceedings of SPIE, the International Society for Optical Engineering, 6966. SPIE: Bellingham. ISBN 9780819471574. , meer
In: Proceedings of SPIE, the International Society for Optical Engineering. SPIE: Bellingham, WA. ISSN 0277-786X; e-ISSN 1996-756X, meer
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| Beschikbaar in | Auteurs |
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Documenttype: Congresbijdrage
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| Author keywords |
Support Vector Machines (SVM), Principal Component Analysis (PCA), Imaging spectroscopy, anomaly detection, material identification |
| Auteurs | | Top |
- Garcia-Allende, P.B.
- Anabitarte, F.
- Conde, O.M.
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- Mirapeix, J.
- Madruga, F.J.
- Lopez-Higuera, J.M.
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| Abstract |
A processing methodology based on Support Vector Machines is presented in this paper for the classification of hyperspectral spectroscopic images. The accurate classification of the images is used to perform on-line material identification in industrial environments. Each hyperspectral image consists of the diffuse reflectance of the material under study along all the points of a line of vision. These images are measured through the employment of two imaging spectrographs operating at Vis-NIR, from 400 to 1000 nm, and NIR, from 1000 to 2400 nm, ranges of the spectrum, respectively. The aim of this work is to demonstrate the robustness of Support Vector Machines to recognise certain spectral features of the target. Furthermore, research has been made to find the adequate SVM configuration for this hyperspectral application. In this way, anomaly detection and material identification can be efficiently performed. A classifier with a combination of a Gaussian Kernel and a non linear Principal Component Analysis, namely k-PCA is concluded as the best option in this particular case. Finally, experimental tests have been carried out with materials typical of the tobacco industry (tobacco leaves mixed with unwanted spurious materials, such as leathers, plastics, etc.) to demonstrate the suitability of the proposed technique |
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