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Toward Optimum Fusion of Thermal Hyperspectral and Visible Images in Classification of Urban Area

Samadzadegan, Farhad and Hasani, Hadiseh and Reinartz, Peter (2017) Toward Optimum Fusion of Thermal Hyperspectral and Visible Images in Classification of Urban Area. Photogrammetric Engineering and Remote Sensing, 83 (4), pp. 269-280. American Society for Photogrammetry and Remote Sensing. DOI: 10.14358/PERS.83.4.87 ISBN ISSN 0099-1112 ISSN 0099-1112

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Abstract

Recently, classification of urban area based on multi-sensor fusion has been widely investigated. In this paper, the potential of using visible (VIS) and thermal infrared (TIR) hyperspectral images fusion for classification of urban area is evaluated. For this purpose, comprehensive spatial-spectral feature space is generated which includes vegetation index, differential morphological profile (DMP), attribute profile (AP), texture, geostatistical features, structural feature set (SFS) and local statistical descriptors from both datasets in addition to original datasets. Although Support Vector Machine (SVM) is an appropriate tool in the classification of high dimensional feature space, its performance is significantly affected by its parameters and feature space. Cuckoo search (CS) optimization algorithm with mixed binary-continuous coding is proposed for feature selection and SVM parameter determination simultaneously. Moreover, the significance of each selected feature category in the classification of a specific object is verified. Accuracy assessment on two subsets shows that stacking of VIS and TIR bands can improve the classification performance to 87 percent and 82 percent for two subsets, compare to VIS image (72 percent and 80 percent) and TIR image (50 percent and 56 percent). However, the optimum results obtained based on the proposed method which gains 94 percent and 92 percent. Furthermore, results show that using TIR beside VIS image improves classification accuracy of roads and buildings in urban area.

Item URL in elib:https://elib.dlr.de/113393/
Document Type:Article
Title:Toward Optimum Fusion of Thermal Hyperspectral and Visible Images in Classification of Urban Area
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Samadzadegan, FarhadUniversität Teheran, IranUNSPECIFIED
Hasani, HadisehUniversität Teheran, IranUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:4 April 2017
Journal or Publication Title:Photogrammetric Engineering and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:83
DOI :10.14358/PERS.83.4.87
Page Range:pp. 269-280
Editors:
EditorsEmail
UNSPECIFIEDAmerican Society of Photogrammetry and Remote Sensing
Publisher:American Society for Photogrammetry and Remote Sensing
ISSN:0099-1112
ISBN:ISSN 0099-1112
Status:Published
Keywords:hyperspectral, visible Images, classification, urban area
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), R - Vorhaben hochauflösende Fernerkundungsverfahren, Vorhaben Optical Remote Sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:07 Aug 2017 12:16
Last Modified:06 Sep 2019 15:17

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