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Fusion of hyperspectral and LIDAR data using decisiontemplate-based fuzzy multiple classifier system

Bigdeli, Behnaz and Samadzadegan, Farhad and Reinartz, Peter (2015) Fusion of hyperspectral and LIDAR data using decisiontemplate-based fuzzy multiple classifier system. International Journal of Applied Earth Observation and Geoinformation, 38, pp. 309-320. Elsevier. DOI: doi:10.1016/j.jag.2015.01.017 ISBN 0303-2434 (ISSN) ISSN 0303-2434

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Official URL: http://www.sciencedirect.com/science/article/pii/S0303243415000306

Abstract

Regarding to the limitations and benefits of remote sensing sensors, fusion of remote sensing data from multiple sensors such as hyperspectral and LIDAR (light detection and ranging) is effective at land cover classification. Hyperspectral images (HSI) provide a detailed description of the spectral signatures of classes, whereas LIDAR data give height detailed information. However, because of the more complexities and mixed information in LIDAR and HSI, traditional crisp classification methods could not be more efficient. In this situation, fuzzy classifiers could deliver more satisfactory results than crisp classification approaches. Also, referring to the limitation of single classifiers, multiple classifier system (MCS) may exhibit better performance in the field of multi-sensor fusion. This paper presents a fuzzy multiple classifier system for fusions of HSI and LIDAR data based on decision template (DT). After feature extraction and feature selection on each data, all selected features of both data are applied on a cube. Then classifications were performed by fuzzy k-nearest neighbour (FKNN) and fuzzy maximum likelihood (FML) on cube of features. Finally, a fuzzy decision fusion method is utilized to fuse the results of fuzzy classifiers. In order to assess fuzzy MCS proposed method, a crisp MCS based on support vector machine (SVM), KNN and maximum likelihood (ML) as crisp classifiers and naive Bayes (NB) as crisp classifier fusion method is applied on selected cube feature. A co-registered HSI and LIDAR data set from Houston of USA was available to examine the effect of proposed MCSs. Fuzzy MCS on HSI and LIDAR data provide interesting conclusions on the effectiveness and potentialities of the joint use of these two data.

Item URL in elib:https://elib.dlr.de/95562/
Document Type:Article
Title:Fusion of hyperspectral and LIDAR data using decisiontemplate-based fuzzy multiple classifier system
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bigdeli, BehnazUniversity of Tehran, Tehran, IranUNSPECIFIED
Samadzadegan, FarhadUniversity of Tehran, Tehran, IranUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Date:June 2015
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:38
DOI :doi:10.1016/j.jag.2015.01.017
Page Range:pp. 309-320
Editors:
EditorsEmail
van der Meer, FreekFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands
Publisher:Elsevier
Series Name:Elsevier International Journals
ISSN:0303-2434
ISBN:0303-2434 (ISSN)
Status:Published
Keywords:LIDAR; Hyperspectral; Fuzzy classification; Multiple classifier system; Sensor fusion
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:20 Mar 2015 16:11
Last Modified:06 Sep 2019 15:16

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