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Hyperspectral Image Classification with Kernels

Hyperspectral Image Classification with Kernels

Lorenzo Bruzzone, Luis Gomez-Chova, Mattia Marconcini, Gustavo Camps-Valls
Copyright: © 2007 |Pages: 25
ISBN13: 9781599040424|ISBN10: 1599040425|ISBN13 Softcover: 9781599040431|EISBN13: 9781599040448
DOI: 10.4018/978-1-59904-042-4.ch016
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MLA

Bruzzone, Lorenzo, et al. "Hyperspectral Image Classification with Kernels." Kernel Methods in Bioengineering, Signal and Image Processing, edited by Gustavo Camps-Valls, et al., IGI Global, 2007, pp. 374-398. https://doi.org/10.4018/978-1-59904-042-4.ch016

APA

Bruzzone, L., Gomez-Chova, L., Marconcini, M., & Camps-Valls, G. (2007). Hyperspectral Image Classification with Kernels. In G. Camps-Valls, J. Rojo-Alvarez, & M. Martinez-Ramon (Eds.), Kernel Methods in Bioengineering, Signal and Image Processing (pp. 374-398). IGI Global. https://doi.org/10.4018/978-1-59904-042-4.ch016

Chicago

Bruzzone, Lorenzo, et al. "Hyperspectral Image Classification with Kernels." In Kernel Methods in Bioengineering, Signal and Image Processing, edited by Gustavo Camps-Valls, Jose Luis Rojo-Alvarez, and Manel Martinez-Ramon, 374-398. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-042-4.ch016

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Abstract

The information contained in hyperspectral images allows the characterization, identification, and classification of land covers with improved accuracy and robustness. However, several critical problems should be considered in the classification of hyperspectral images, among which are (a) the high number of spectral channels, (b) the spatial variability of the spectral signature, (c) the high cost of true sample labeling, and (d) the quality of data. Recently, kernel methods have offered excellent results in this context. This chapter reviews the state-of-the-art hyperspectral image classifiers, presents two recently proposed kernel-based approaches, and systematically discusses the specific needs and demands of this field.

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