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A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data

Hasani, Hadiseh and Samadzadegan, Farhad and Reinartz, Peter (2017) A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data. European Journal of Remote Sensing, 50 (1), pp. 222-236. Taylor & Francis. DOI: 10.1080/22797254.2017.1314179 ISSN 2279-7254

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Official URL: http://www.tandfonline.com/doi/full/10.1080/22797254.2017.1314179

Abstract

One of the most sophisticated recent data fusions in remote sensing has involved the use of LiDAR and hyperspectral data. Feature-level fusion strategy is applied based on extraction of several recent proposed spectral and structural features from hyperspectral and LiDAR data, respectively. In order to optimize classification performance, feature selection and determination of classifier parameters are carried out simultaneously. Referring to complexity of search space, cuckoo search as a powerful metaheuristic optimization algorithm is applied. Experiments show that the proposed method can improve the overall classification accuracy up to 6% with respect to only hyperspectral imagery. The obtained results show the classification improvement for the tree, residential and commercial classes is about 4%, 21% and 35%, respectively.

Item URL in elib:https://elib.dlr.de/112898/
Document Type:Article
Title:A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hasani, HadisehUNSPECIFIEDUNSPECIFIED
Samadzadegan, FarhadUNSPECIFIEDUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:18 April 2017
Journal or Publication Title:European Journal of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:No
Volume:50
DOI :10.1080/22797254.2017.1314179
Page Range:pp. 222-236
Editors:
EditorsEmail
Marchetti, MarcoUniversità degli Studi del Molise, Italy
Publisher:Taylor & Francis
ISSN:2279-7254
Status:Published
Keywords:Classification, urban area, hyperspectral, LiDAR, cuckoo search, SVM
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:INVALID USER
Deposited On:30 Jun 2017 14:24
Last Modified:22 Aug 2019 05:03

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