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Classification of Hyperspectral Data Using an AdaBoostSVM Technique Applied on Band Clusters

Ramzi, Pouria and Samadzadegan, Farhad and Reinartz, Peter (2014) Classification of Hyperspectral Data Using an AdaBoostSVM Technique Applied on Band Clusters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (6), pp. 2066-2079. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2013.2292901 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6691910

Abstract

Supervised classification of hyperspectral image data using conventional statistical classification methods is difficult because a sufficient number of training samples is often not available for the wide range of spectral bands. In addition, spectral bands are usually highly correlated and contain data redundancies because of the short spectral distance between the adjacent bands. To address these limitations, a multiple classifier system based on Adaptive Boosting (AdaBoost) is proposed and evaluated to classify hyperspectral data. In this method, the hyperspectral datasets are first split into several band clusters based on the similarities between the contiguous bands. In an AdaBoost classification system, the redundant and noninformative bands in each cluster are then removed using an optimal band selection technique. Next, a support vector machine (SVM) is applied to each refined cluster based on the classification results of previous clusters, and the results of these classifiers are fused using the weights obtained from the AdaBoost processing. Experimental results with standard hyperspectral datasets clearly demonstrate the superiority of the proposed algorithm with respect to both global and class accuracies, when compared to another ensemble classifiers such as simple majority voting and Naïve Bayes to combine decisions from each cluster, a standard SVM applied on the selected bands of entire datasets and on all the spectral bands. More specifically, the proposed method performs better than other approaches, especially in datasets which contain classes with greater complexity and fewer available training samples.

Item URL in elib:https://elib.dlr.de/91458/
Document Type:Article
Title:Classification of Hyperspectral Data Using an AdaBoostSVM Technique Applied on Band Clusters
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ramzi, Pouriapramzi (at) ut.ac.irUNSPECIFIED
Samadzadegan, Farhadsamadz (at) ut.ac.irUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Date:1 August 2014
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:7
DOI :10.1109/JSTARS.2013.2292901
Page Range:pp. 2066-2079
Editors:
EditorsEmail
Chanussot, Jocelynjocelyn.chanussot@gipsa-lab.grenoble-inp.fr
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Adaptive Boosting (AdaBoost), band clustering, hyperspectral data, multiple classifier systems (MCSs), support, vector machines (SVMs)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By:INVALID USER
Deposited On:10 Nov 2014 08:56
Last Modified:08 Mar 2018 18:31

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