%0 Generic %1 dlr116786 %A Kumar-Babu, Dinesh %A Kaufmann, Christof %A Schmidt, Marco %A Dahms, Thorsten %A Conrad, Christopher %B SPIE %D 2017 %J Spie Digital Library %K conference oral talk Dahms LSFE %P 1--16 %T Semi-Autonomous Remote Sensing Time Series Generation Tool %U http://elib.dlr.de/116786/ %X "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."

@presentation{dlr116786, 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."}, added-at = {2020-09-11T12:04:50.000+0200}, author = {Kumar-Babu, Dinesh and Kaufmann, Christof and Schmidt, Marco and Dahms, Thorsten and Conrad, Christopher}, biburl = {https://www.bibsonomy.org/bibtex/21230befd246c702e4d869b21d8b95481/earthobs_uniwue}, booktitle = {SPIE}, interhash = {8c6fba20b97361097099707d9861231c}, intrahash = {1230befd246c702e4d869b21d8b95481}, journal = {Spie Digital Library}, keywords = {conference oral talk Dahms LSFE}, pages = {1--16}, timestamp = {2020-11-18T22:08:33.000+0100}, title = {Semi-Autonomous Remote Sensing Time Series Generation Tool}, url = {http://elib.dlr.de/116786/}, year = 2017 }