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

Fichtelmann, Bernd and Borg, Erik and Günther, Kurt P. (2014) Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to MERIS Data. In: Computational Science and Its Applications – ICCSA 2014, Part I, LNCS 8579, pp. 393-407. ICCSA 2014, 30.6.-3.7.2014, Guimarães, Portugal. ISBN 978-3-319-09143-3

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Abstract

Abstract. In many global applications of remote sensing land-water masks can improve the interpretation results. Their use can be of advantage to distinguish between different types of dark areas (e.g. cloud or topographic shadows, burned areas, coniferous forests, water areas). On one hand, water bodies cannot always be classified exactly on basis of available remote sensing data. On the other hand static land-water masks of different quality are available. But the main deficiencies are caused by the fact that land-water masks represent only a temporal snapshot of the water bodies. A dynamic self-learning water masking approach was developed at first for AATSR data to combine the advantages of static mask with results of pre-classifications. This paper presents the adaption of this procedure for MERIS remote sensing data. As before with AATSR data the aim consists in integrating high-quality water masks in processing chains for deriving value-added remote sensing data products. The results for some regional examples demonstrate the quality of masks and the advantages to conventional water masking algorithms. Furthermore, it will be discussed, that it is useful for a global water mask of high quality to integrate further special masks as cloud or in particular terrain shadow masks. At least, the land-water mask plays not only an important role in complex processing chains itself is the result of a complex procedure. Beside the results have shown successful transfer of a developed processing scheme for operational deriving of actual land-water masks to data of a second sensor, the adaption to further sensors or the adaption of the processor to other object types as e.g. forest will be possible in future as part of operational monitoring systems.

Item URL in elib:https://elib.dlr.de/89740/
Document Type:Conference or Workshop Item (Speech)
Title:Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to MERIS Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Fichtelmann, Berndbernd.fichtelmann (at) dlr.deUNSPECIFIED
Borg, Erikerik.borg (at) dlr.deUNSPECIFIED
Günther, Kurt P.kurt.guenther (at) dlr.deUNSPECIFIED
Date:July 2014
Journal or Publication Title:Computational Science and Its Applications – ICCSA 2014, Part I, LNCS 8579
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 393-407
Editors:
EditorsEmail
Murgante, B.UNSPECIFIED
Misra, S.UNSPECIFIED
Rocha, A.M.A.C.UNSPECIFIED
Torre, C.UNSPECIFIED
Rocha, J.G.UNSPECIFIED
Falcão, M.I.UNSPECIFIED
Taniar, D.UNSPECIFIED
Apduhan, B.O.UNSPECIFIED
Gervasi, O.UNSPECIFIED
Series Name:Springer Cham Heidelberg NewYork Dordrecht London
ISBN:978-3-319-09143-3
Status:Published
Keywords:self-learning algorithm, land-water mask, interpretation, remote sensing, MERIS data, cloud cover
Event Title:ICCSA 2014
Event Location:Guimarães, Portugal
Event Type:international Conference
Event Dates:30.6.-3.7.2014
Organizer:University of Minho, Portugal
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
German Remote Sensing Data Center > National Ground Segment
Deposited By: Fichtelmann, Dr.rer.nat. Bernd
Deposited On:17 Jul 2014 09:50
Last Modified:17 Jul 2014 09:50

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