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Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to SPOT VEGETATION Data

Fichtelmann, Bernd and Günther, Kurt P and Borg, Erik (2015) Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to SPOT VEGETATION Data. In: Computational Science an Its Applications - ICCSA 2015, Part IV, LNCS 9158, pp. 177-192. Springer International Publishing Switzerland. 15th International Conference on Computational Science and its Applications - ICCSA 2015, 22.-25. Juni 2015, Banff, Canada. DOI: 10.1007/978-3-319-21410-8_14 ISBN 978-3-319-21409-2 (Print) 978-3-319-21410-8 (Online)

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Official URL: http://link.springer.com/chapter/10.1007/978-3-319-21410-8_14#

Abstract

Within the ESA CCI “Fire Disturbance” project a dynamic self-learning water masking approach originally developed for AATSR data was modified for MERIS-FR(S) and MERIS-RR data and now for SPOT VEGETATION (VGT) data. The primary goal of the development was to apply for all sensors the same generic principles by combining static water masks on a global scale with a self-learning algorithm. Our approach results in the generation of a dynamic water mask which helps to distinguish dark burned area objects from other different types of dark areas (e.g. cloud or topographic shadows, coniferous forests). The use of static land-water masks includes the disadvantage that land-water masks represent only a temporal snapshot of the water bodies. Regional results demonstrate the quality of the dynamic water mask. In addition the advantages to conventional water masking algorithms are shown. Furthermore, the dynamic water masks of AATSR, MERIS and VGT for the same region are presented and discussed together with the use of more detailed static water masks.

Item URL in elib:https://elib.dlr.de/96936/
Document Type:Conference or Workshop Item (Speech)
Title:Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to SPOT VEGETATION Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Fichtelmann, Berndbernd.fichtelmann (at) dlr.deUNSPECIFIED
Günther, Kurt Pkurt.guenther (at) dlr.deUNSPECIFIED
Borg, Erikerik.borg (at) dlr.deUNSPECIFIED
Date:25 June 2015
Journal or Publication Title:Computational Science an Its Applications - ICCSA 2015, Part IV, LNCS 9158
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1007/978-3-319-21410-8_14
Page Range:pp. 177-192
Editors:
EditorsEmail
Gervasi, Oswaldoosvaldo@unipg.it
Murgante, Beniaminobeniamino.murgante@unibas.it
Misra, Sanjaysmisra@futminna.edu.ng
Gavrilova, Marina L.mgavrilo@ucalgary.ca
Rocha, Ana Maria Alves Countinhoarocha@dps.uminho.pt
Torre, Carmelotorre@poliba.it
Taniar, Daviddavid.taniar@infotech.monash.edu.au
Apduhan, Bernady O.bob@is.kyusan-u.ac.jp
Publisher:Springer International Publishing Switzerland
ISBN:978-3-319-21409-2 (Print) 978-3-319-21410-8 (Online)
Status:Published
Keywords:Self-learning algorithm, Land-water mask, Interpretation, Remote sensing, VGT data, Cloud cover
Event Title:15th International Conference on Computational Science and its Applications - ICCSA 2015
Event Location:Banff, Canada
Event Type:international Conference
Event Dates:22.-25. Juni 2015
Organizer:University of Calgary, Calgary, Canada
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 Fernerkundung der Landoberfläche (old)
Location: Neustrelitz
Institutes and Institutions:German Remote Sensing Data Center > National Ground Segment
German Remote Sensing Data Center > Land Surface
Deposited By: Fichtelmann, Dr.rer.nat. Bernd
Deposited On:04 Aug 2015 10:47
Last Modified:04 Aug 2015 10:47

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