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Dynamic self-learning water-masking algorithm for AATSR, MERIS, and SPOT VEGETATION

Borg, Erik and Fichtelmann, Bernd and Günther, Kurt P. (2017) Dynamic self-learning water-masking algorithm for AATSR, MERIS, and SPOT VEGETATION. International Journal of Business Intelligence and Data Mining, 12 (2), pp. 95-118. Inderscience Publishers. DOI: DOI: 10.1504/IJBIDM.2017.10004159 ISBN ISSN print: 1743-8187, ISSN online: 1743-8195 ISSN 1743-8187

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Official URL: http://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijbidm

Abstract

Within the ESA CCI Fire Disturbance project (Guenther et al., 2012), a dynamic self-learning water masking approach was developed for AATSR, MERIS-FR(S), MERIS-RR, and for SPOT VEGETATION (SPOT-VGT) data. The primary goal of the development was to find for all sensors a generic algorithm 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 burned areas from other dark areas as, e.g., cloud or topographic shadows or coniferous forests. The use of static water masks as training areas for the learning algorithm must take into account that small and shallow water bodies may change in time and that a precise geo-location of the static water mask and the scene under investigation is mandatory. The comparison of the water masks derived from all sensors for a region in Kazakhstan demonstrates the quality of the new dynamic water masks. In addition, the advantages to other water masking algorithms (MOD44W, Hansen_GFC or IDEPIX) are shown. Furthermore, the dynamic water masks of AATSR, MERIS and SPOT-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/112351/
Document Type:Article
Title:Dynamic self-learning water-masking algorithm for AATSR, MERIS, and SPOT VEGETATION
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Borg, ErikErik.Borg (at) dlr.deUNSPECIFIED
Fichtelmann, BerndBernd.Fichtelmann (at) dlr.deUNSPECIFIED
Günther, Kurt P.kurt.guenther (at) dlr.deUNSPECIFIED
Date:2017
Journal or Publication Title:International Journal of Business Intelligence and Data Mining
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:12
DOI :DOI: 10.1504/IJBIDM.2017.10004159
Page Range:pp. 95-118
Editors:
EditorsEmail
Pratama, MahardhikaInderscience Enterprises Ltd.
Publisher:Inderscience Publishers
ISSN:1743-8187
ISBN:ISSN print: 1743-8187, ISSN online: 1743-8195
Status:Published
Keywords:self-learning algorithm; land-water mask; interpretation; remote sensing; MERIS; AATSR; SPOT-VEGETATION; global.
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 Datenakquisition und Produktgenerierung (old)
Location: Neustrelitz
Institutes and Institutions:German Remote Sensing Data Center > National Ground Segment
Deposited By: Borg, Dr.rer.nat. Erik
Deposited On:04 Jul 2017 11:34
Last Modified:08 Mar 2018 18:40

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