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Feature grouping-based multiple fuzzy classifier system for fusion of hyperspectral and LIDAR data

Bigdeli, Behnaz and Samadzadegan, Farhad and Reinartz, Peter (2014) Feature grouping-based multiple fuzzy classifier system for fusion of hyperspectral and LIDAR data. Journal of Applied Remote Sensing, 8 (1), pp. 1-16. Society of Photo-optical Instrumentation Engineers (SPIE). DOI: 10.1117/1.JRS.8.083509 ISSN 1931-3195

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Official URL: http://remotesensing.spiedigitallibrary.org/article.aspx?articleid=2022245


Interest in the joint use of different data from multiple sensors has been increased for classification applications. This is because the fusion of different information can produce a better understanding of the observed site. In this field of study, the fusion of light detection and ranging (LIDAR) and passive optical remote sensing data for classification of land cover has attracted much attention. This paper addressed the use of a combination of hyperspec- tral (HS) and LIDAR data for land cover classification. HS images provide a detailed description of the spectral signatures of classes, whereas LIDAR data give detailed information about the height but no information for the spectral signatures. This paper presents a multiple fuzzy clas- sifier system for fusion of HS and LIDAR data. The system is based on the fuzzy K-nearest neighbor (KNN) classification of two data sets after application of feature grouping on them. Then a fuzzy decision fusion method is applied to fuse the results of fuzzy KNN clas- sifiers. An experiment was carried out on the classification of HS and LIDAR data from Houston, USA. The proposed fuzzy classifier ensemble system for HS and LIDAR data provide interesting conclusions on the effectiveness and potentials of the joint use of these two data. Fuzzy classifier fusion on these two data sets improves the classification results when compared with independent single fuzzy classifiers on each data set. The fuzzy proposed method repre- sented the best accuracy with a gain in overall accuracy of 93%.

Item URL in elib:https://elib.dlr.de/93761/
Document Type:Article
Additional Information:© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.8.083509]
Title:Feature grouping-based multiple fuzzy classifier system for fusion of hyperspectral and LIDAR data
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bigdeli, BehnazUniversity of Tehran, IranUNSPECIFIED
Samadzadegan, FarhadUniversity of Tehran, IranUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Date:5 November 2014
Journal or Publication Title:Journal of Applied Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1117/1.JRS.8.083509
Page Range:pp. 1-16
Chan, Ni-BinUniversity of Central Florida, USA
Publisher:Society of Photo-optical Instrumentation Engineers (SPIE)
Keywords:LIDAR data; hyperspectral data; feature grouping; classifier fusion; fuzzy 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 On:17 Dec 2014 09:37
Last Modified:31 Jul 2019 19:50

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