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A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area

Abdi, Ghasem and Samadzadegan, Farhad and Reinartz, Peter (2017) A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area. European Journal of Remote Sensing, 50 (1), pp. 414-427. Taylor & Francis. DOI: 10.1080/22797254.2017.1348914 ISSN 2279-7254

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Official URL: http://www.tandfonline.com/loi/tejr20?open=50&year=2017&repitition=0#vol_50_2017


Multi-sensor data fusion has become more and more popular for classification applications. The fusion of multisource remote-sensing data can provide more information about the same observed site results in a superior comprehension of the scene. In this field of study, a combination of very high-resolution data collected by a digital color camera and a new coarse resolution hyperspectral data in the long-wave infrared range for urban land-cover classification has been extensively enticed much consideration and turned into a research hot spot in image analysis and data fusion research community. In this paper, a decision-based multi-sensor classification system is proposed to completely use the advantages of both sensors to attain enhanced land-cover classification results. In this context, spectral, textural and spatial features are extracted for the proposed multilevel classification. Then, a land-cover separability preprocessing is employed to identify how the proposed method can fully utilize the sensor advantages. Next, a support vector machine is applied to classify road classes by using thermal hyperspectral image data; plants, roofs and bare soils are classified by the joint use of sensors via Dempster-Shafer classifier fusion. Finally, an object-based post-processing is employed to improve the classification results. Experiments carried out on the dataset of 2014 IEEE GRSS data fusion contest indicate the superiority of the proposed methodology for the potentialities and possibilities of the joint utilization of sensors and refine the classification outcomes when evaluated against single sensor data. Meanwhile, the obtained classification accuracy can be a competitor against the results issued by the 2014 IEEE GRSS data fusion contest.

Item URL in elib:https://elib.dlr.de/113949/
Document Type:Article
Title:A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Abdi, Ghasemghasem.abdi (at) ut.ac.irUNSPECIFIED
Samadzadegan, Farhadsamadz (at) ut.ac.irUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:12 July 2017
Journal or Publication Title:European Journal of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.1080/22797254.2017.1348914
Page Range:pp. 414-427
UNSPECIFIEDTaylor and Francis Group
Publisher:Taylor & Francis
Keywords:Decision-level fusion; landcover classification; multi-sensor fusion; support vector machine; thermal hyperspectral
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
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:29 Sep 2017 17:00
Last Modified:14 Dec 2019 04:23

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