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

Fichtelmann, Bernd and 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, pp. 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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Official URL: http://rd.springer.com/book/10.1007/978-3-642-31137-6/page/1

Abstract

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.

Document Type:Conference or Workshop Item (Speech, Paper)
Title:A new Self-learning Algorithm for Dynamic Classification of Water Bodies
Authors:
AuthorsInstitution or Email of Authors
Fichtelmann, Berndbernd.fichtelmann@dlr.de
Borg, Erikerik.borg@dlr.de
Date:2012
Journal or Publication Title:Computational Science an Its Applications - ICCSA 2012, Part III, LNCS 7335
Refereed publication:Yes
In ISI Web of Science:No
Volume:Procee
Page Range:pp. 457-470
Editors:
EditorsEmail
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
Publisher:Springer Heidelberg Dordrecht London New York
ISSN:www
ISBN:978-3-642-31136-9
Status:Published
Keywords:self-learning algorithm, land-water mask, interpretation, remote sensing
Event Title:ICCSA 2012
Event Location:Salvador de Bahia, Brasilien
Event Type:international Conference
Event Dates:18.-21. Juni 2012
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignement
DLR - Research theme (Project):R -- no assignement
Location: Neustrelitz
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Dr.rer.nat. Bernd Fichtelmann
Deposited On:30 Jul 2012 09:22
Last Modified:12 Dec 2013 21:42

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