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.
Item URL in elib: | https://elib.dlr.de/76300/ | ||||||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech, Paper) | ||||||||||||||||||||||||
Title: | A new Self-learning Algorithm for Dynamic Classification of Water Bodies | ||||||||||||||||||||||||
Authors: |
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Date: | 2012 | ||||||||||||||||||||||||
Journal or Publication Title: | Computational Science an Its Applications - ICCSA 2012, Part III, LNCS 7335 | ||||||||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||||||||
Open Access: | Yes | ||||||||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||||||||
In SCOPUS: | No | ||||||||||||||||||||||||
In ISI Web of Science: | No | ||||||||||||||||||||||||
Volume: | Procee | ||||||||||||||||||||||||
Page Range: | pp. 457-470 | ||||||||||||||||||||||||
Editors: |
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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 assignment | ||||||||||||||||||||||||
DLR - Research theme (Project): | R - no assignment | ||||||||||||||||||||||||
Location: | Neustrelitz | ||||||||||||||||||||||||
Institutes and Institutions: | German Remote Sensing Data Center | ||||||||||||||||||||||||
Deposited By: | Fichtelmann, Dr.rer.nat. Bernd | ||||||||||||||||||||||||
Deposited On: | 30 Jul 2012 09:22 | ||||||||||||||||||||||||
Last Modified: | 31 Jul 2019 19:36 |
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