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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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/ | ||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||
Title: | Semi-Autonomous Remote Sensing Time Series Generation Tool | ||||||||||||||||||
Authors: |
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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 - Earth Observation | ||||||||||||||||||
DLR - Research theme (Project): | R - Geoscientific remote sensing and GIS methods | ||||||||||||||||||
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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