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Open AccessArticle

Assessment of Component Selection Strategies in Hyperspectral Imagery

1
Instituto de Oceanografía y Cambio Global, IOCAG, Universidad de Las Palmas de Gran Canaria, ULPGC, Parque Científico Tecnológico Marino de Taliarte, s/n, 35214 Telde, Spain
2
Center of Biomedical Technology, Universidad Politécnica de Madrid, UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(12), 666; https://doi.org/10.3390/e19120666
Received: 23 October 2017 / Revised: 30 November 2017 / Accepted: 1 December 2017 / Published: 5 December 2017
(This article belongs to the Special Issue Selected Papers from IWOBI—Entropy-Based Applied Signal Processing)
Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the ’Hughes’ phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification. View Full-Text
Keywords: remote sensing; hyperspectral sensor; feature-extraction; texture measurement; classification; ecosystem management remote sensing; hyperspectral sensor; feature-extraction; texture measurement; classification; ecosystem management
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Ibarrola-Ulzurrun, E.; Marcello, J.; Gonzalo-Martin, C. Assessment of Component Selection Strategies in Hyperspectral Imagery. Entropy 2017, 19, 666.

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