Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/25107
Title: Assessment of component selection strategies in hyperspectral imagery
Authors: Ibarrola-Ulzurrun, E. 
Marcello, Javier 
Gonzalo Martin,Consuelo 
UNESCO Clasification: 220921 Espectroscopia
330412 Dispositivos de control
220990 Tratamiento digital. Imágenes
Keywords: Remote sensing
Hyperspectral sensor
Feature-extraction
Texture measurement
Classification, et al
Issue Date: 2017
Journal: Entropy 
Abstract: Hyperspectral imagery (HSI) integratesmany continuous and narrowbands 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),MinimumNoise 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 CASIHSI regarding the spectral regions covering the electromagnetic spectrum. The components selected fromthe 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.
URI: http://hdl.handle.net/10553/30709
ISSN: 1099-4300
DOI: 10.3390/e19120666
Source: Entropy [ISSN 1099-4300], v. 19 (12), article number 666
Rights: by-nc-nd
URL: http://api.elsevier.com/content/abstract/scopus_id/85038369900
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