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Semi-Autonomous Remote Sensing Time Series Generation Tool

Kumar-Babu, Dinesh and Kaufmann, Christof and Schmidt, Marco and Dahms, Thorsten and Conrad, Christopher (2017) Semi-Autonomous Remote Sensing Time Series Generation Tool. In: Spie Digital Library, pp. 1-16. SPIE, 4.10.2017, Warschau, Polen. DOI: 10.1117/12.2278213

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Official URL: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10427/2278213/Semi-autonomous-remote-sensing-time-series-generation-tool/10.1117/12.2278213.short

Abstract

"High spatial and temporal resolution data is vital for crop monitoring and phenology change detection. Due to the lack of satellite architecture and frequent cloud cover issues, availability of daily high spatial data is still far from reality. Remote sensing time series generation of high spatial and temporal data by data fusion seems to be a practical alternative. However, it is not an easy process, since it involves multiple steps and also requires multiple tools. In this paper, a framework of Geo Information System (GIS) based tool is presented for semi-autonomous time series generation. This tool will eliminate the difficulties by automating all the steps and enable the users to generate synthetic time series data with ease. Firstly, all the steps required for the time series generation process are identified and grouped into blocks based on their functionalities. Later two main frameworks are created, one to perform all the pre-processing steps on various satellite data and the other one to perform data fusion to generate time series. The two frameworks can be used individually to perform specific tasks or they could be combined to perform both the processes in one go. This tool can handle most of the known geo data formats currently available which makes it a generic tool for time series generation of various remote sensing satellite data. This tool is developed as a common platform with good Interface which provides lot of functionalities to enable further development of more remote sensing applications. A detailed description on the capabilities and the advantages of the frameworks are given in this paper."

Item URL in elib:https://elib.dlr.de/116786/
Document Type:Conference or Workshop Item (Speech)
Title:Semi-Autonomous Remote Sensing Time Series Generation Tool
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Kumar-Babu, Dineshdinesh.kumar-babu (at) hs-bochum.deUNSPECIFIED
Kaufmann, ChristofUNSPECIFIEDUNSPECIFIED
Schmidt, Marcomarco.schmidt (at) hs-bochum.deUNSPECIFIED
Dahms, Thorstenthorsten.dahms (at) uni-wuerzburg.deUNSPECIFIED
Conrad, ChristopherChristopher.Conrad (at) uni-wuerzburg.deUNSPECIFIED
Date:2017
Journal or Publication Title:Spie Digital Library
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1117/12.2278213
Page Range:pp. 1-16
Series Name:Proceedings Volume 10427, Image and Signal Processing for Remote Sensing XXIII
Status:Published
Keywords:monitoring and phenology change detection, remote sensing time series, Generation tool
Event Title:SPIE
Event Location:Warschau, Polen
Event Type:international Conference
Event Dates:4.10.2017
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 Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:11 Dec 2017 09:21
Last Modified:15 Mar 2018 14:08

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