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A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data

Bigdeli, Behnaz and Samadzadegan, Farhad and Reinartz, Peter (2013) A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data. Journal of the Indian Society of Remote Sensing, 41 (4), pp. 763-776. Springer. DOI: 10.1007/s12524-013-0286-z ISBN 0255-660X (p) 0974-3006 (e) ISSN 0255-660X


Official URL: http://www.springer.com/earth+sciences+and+geography/journal/12524


With recent technological advances in remote sensing sensors and systems, very highdimensional hyperspectral data are available for a better discrimination among different complex landcover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or ‘curse of dimensionality’ in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated. Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifier in these situations, Multiple Classifier Systems (MCS) may have better performance than single classifier. This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. The proposed method uses the band grouping process based on a modified mutual information strategy to split data into few band groups. After the band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, the proposed approach applies SVM on each band group that is produced in a previous step. Finally, Naive Bayes (NB) as a classifier fusion method combines decisions of SVM classifiers. Experimental results on two common hyperspectral data sets show that the proposed method improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods.

Item URL in elib:https://elib.dlr.de/83313/
Document Type:Article
Title:A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bigdeli, Behnazbigdeli (at) ut.ac.irUNSPECIFIED
Samadzadegan, Farhadsamadz (at) ut.ac.irUNSPECIFIED
Reinartz, PeterPeter.Reinartz (at) dlr.deUNSPECIFIED
Date:December 2013
Journal or Publication Title:Journal of the Indian Society of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1007/s12524-013-0286-z
Page Range:pp. 763-776
George, JosephISRO, Ahmedabad, India
Series Name:Journal of the Indian Society of Remote Sensing
ISBN:0255-660X (p) 0974-3006 (e)
Keywords:Hyperspectral, Support, Vector, Machine, Multiple Classifier System, Bayesian Theory
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited On:25 Sep 2013 17:50
Last Modified:31 Jul 2019 19:41

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