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Band Grouping versus Band Clustering in SVM Ensemble Classification of Hyperspectral Imagery

Bigdeli, Behnaz and Samadzadegan, Farhad and Reinartz, Peter (2013) Band Grouping versus Band Clustering in SVM Ensemble Classification of Hyperspectral Imagery. Photogrammetric Engineering and Remote Sensing (PE&RS), 79 (6), pp. 523-534. American Society for Photogrammetry and Remote Sensing. ISSN 0099-1112

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Official URL: http://asprs.org/Photogrammetric-Engineering-and-Remote-Sensing/PE-RS-Journals.html


Due to the dense sampling of spectral signatures of land covers, hyperspectral images have a better discrimination among similar ground cover classes than traditional remote sensing data. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral image classifi cation. In addition, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon. Consequently, traditional classifi cation strategies have often limited performance in classifi cation of hyperspectral imagery. Referring to the limitation of single classifi ers in these situations, classifi er ensemble system may exhibit better performance. This paper presents a method for classifi cation of hyperspectral data based on two concepts of Band Clustering (BC) and Band Grouping (BG) through a Support Vector machine (SVM) ensemble system. The proposed method uses the BC\BG strategies to split data into few band portions. After this step, we applied SVM on each band cluster\group that is produced in previous step. Finally, Naive Bayes as a classifi er fusion method combines the decisions of SVM classifi ers. Experimental results show that the proposed method improves the classification accuracy in comparison to the standard SVM and to feature selection methods.

Item URL in elib:https://elib.dlr.de/82632/
Document Type:Article
Title:Band Grouping versus Band Clustering in SVM Ensemble Classification of Hyperspectral Imagery
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bigdeli, BehnazUniversity of TehranUNSPECIFIED
Samadzadegan, Farhadfarhad.samadzadegan (at) dlr.deUNSPECIFIED
Reinartz, PeterPeter.Reinartz (at) dlr.deUNSPECIFIED
Date:June 2013
Journal or Publication Title:Photogrammetric Engineering and Remote Sensing (PE&RS)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 523-534
Congalton, Russel G.University of New Hampshire
Publisher:American Society for Photogrammetry and Remote Sensing
Keywords:Hyperspectral Imagery, Support Vector Machines, Band Clustering, Ensemble Classification
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 By: Reinartz, Prof. Dr.. Peter
Deposited On:05 Jun 2013 07:22
Last Modified:06 Sep 2019 15:29

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