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A new Self-learning Algorithm for Dynamic Classification of Water Bodies

Fichtelmann, Bernd und Borg, Erik (2012) A new Self-learning Algorithm for Dynamic Classification of Water Bodies. In: Computational Science an Its Applications - ICCSA 2012, Part III, LNCS 7335, Procee, Seiten 457-470. Springer Heidelberg Dordrecht London New York. ICCSA 2012, 18.-21. Juni 2012, Salvador de Bahia, Brasilien. ISBN 978-3-642-31136-9. ISSN www

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Offizielle URL: http://rd.springer.com/book/10.1007/978-3-642-31137-6/page/1

Kurzfassung

In many applications of remote sensing data land-water masks play an important role. In this context they can be a helpful orientation to distinguish dark areas (e.g. cloud shadows, topographic shadows, burned areas, coniferous forests) and water areas. However, water bodies cannot always be classified exactly on basis of available remote sensing data. This fact can be caused by a variety of different physical and biological factors (e.g. chlorophyll, suspended particles, surface roughness, turbid and shallow water and dynamic of water bodies) as well as atmospheric factors (e.g. haze and clouds). On the other hand the best available static water masks also show deficiencies. These are essentially caused by the fact that land-water masks represent only a temporal snapshot of the water bodies distributed worldwide and therefore these masks cannot reflect their dynamic behavior. This paper presents a dynamic self-learning water masking approach for AATSR remote sensing data in the context of integrating high-quality water masks in processing chains for deriving value-added remote sensing data products. As an advantage to conventional water masking algorithms, the proposed approach operates on basis of a static water mask as data base for deriving an optimized dynamic water mask. Significant research effort was spent to develop and validate a dynamic self-learning algorithm and a processing scheme for operational derivation of actual land-water masks as basis for operational interpretation of remote sensing data. Based on this concept actual activities and perspectives for contributions to operational monitoring systems will be presented.

Dokumentart:Konferenzbeitrag (Vortrag, Paper)
Titel:A new Self-learning Algorithm for Dynamic Classification of Water Bodies
Autoren:
AutorenInstitution oder E-Mail-Adresse der Autoren
Fichtelmann, Berndbernd.fichtelmann@dlr.de
Borg, Erikerik.borg@dlr.de
Datum:2012
Erschienen in:Computational Science an Its Applications - ICCSA 2012, Part III, LNCS 7335
Referierte Publikation:Ja
In ISI Web of Science:Nein
Band:Procee
Seitenbereich:Seiten 457-470
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der Herausgeber
Murgante, Beniaminobeniamino.murgante@unibas.it
Gervasi, Osvaldoosvaldo@unipg.it
Misra, Sanjaysmisra@futminna.edu.ng
Nedjah, Nadianadia@eng.uerj.br
Rocha, Ana Maria A,C.arocha@dpsuminho.pt
Taniar, Daviddavid.taniar@infotech.monash.edu.au
Apduhan, Bernady O.bob@is.kyusan-u.ac.jp
Verlag:Springer Heidelberg Dordrecht London New York
ISSN:www
ISBN:978-3-642-31136-9
Status:veröffentlicht
Stichwörter:self-learning algorithm, land-water mask, interpretation, remote sensing
Veranstaltungstitel:ICCSA 2012
Veranstaltungsort:Salvador de Bahia, Brasilien
Veranstaltungsart:internationale Konferenz
Veranstaltungsdatum:18.-21. Juni 2012
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:keine Zuordnung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R - keine Zuordnung
DLR - Teilgebiet (Projekt, Vorhaben):R -- keine Zuordnung
Standort: Neustrelitz
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum
Hinterlegt von: Dr.rer.nat. Bernd Fichtelmann
Hinterlegt am:30 Jul 2012 09:22
Letzte Änderung:12 Dez 2013 21:42

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