Van den Bergh, FWessels, Konrad JMiteff, SVan Zyl, TLGazendam, ADBachoo, AK2013-03-192013-03-192012-08Van den Bergh, F, Wessels, KJ, Miteff, S, Van Zyl, TL, Gazendam, AD and Bachoo, AK. 2012. HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment. International Journal of Remote Sensing, vol. 33(15), pp 4720-47400143-1161http://www.tandfonline.com/doi/abs/10.1080/01431161.2011.638339http://hdl.handle.net/10204/6575Copyright: 2012 Taylor & Francis. This is an ABSTRACT ONLY. The definitive version is published in International Journal of Remote Sensing, vol. 33(15), pp 4720-4740Course resolution earth observation satellites offer large data sets with daily observations at global scales. These data sets represent a rich resource that, because of the high acquisition rate, allows the application of time-series analysis methods. To research the application of these time-series analysis methods to large data sets, it is necessary to turn to high-performance computing (HPC) resources and software designs. This article presents an overview of the development of the HiTempo platform, which was designed to facilitate research into time-series analysis of hyper-temporal sequences of satellite image data. The platform is designed to facilitate the exhaustive evaluation and comparison of algorithms, while ensuring that experiments are reproducible. Early results obtained using applications built within the platform are presented. A sample model-based change detection algorithm based on the extended Kalman filter has been shown to achieve a 97% detection success rate on simulated data sets constructed fromMODIS time series. This algorithm has also been parallelized to illustrate that an entire sequence of MODIS tiles (415 tiles over 9 years) can be processed in under 19 minutes using 32 processors.enKalman filtersEarth observation satellitesData sets analysisRemote-sensing satellite dataMODIS time seriesArticleVan den Bergh, F., Wessels, K. J., Miteff, S., Van Zyl, T., Gazendam, A., & Bachoo, A. (2012). HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment. http://hdl.handle.net/10204/6575Van den Bergh, F, Konrad J Wessels, S Miteff, TL Van Zyl, AD Gazendam, and AK Bachoo "HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment." (2012) http://hdl.handle.net/10204/6575Van den Bergh F, Wessels KJ, Miteff S, Van Zyl T, Gazendam A, Bachoo A. HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment. 2012; http://hdl.handle.net/10204/6575.TY - Article AU - Van den Bergh, F AU - Wessels, Konrad J AU - Miteff, S AU - Van Zyl, TL AU - Gazendam, AD AU - Bachoo, AK AB - Course resolution earth observation satellites offer large data sets with daily observations at global scales. These data sets represent a rich resource that, because of the high acquisition rate, allows the application of time-series analysis methods. To research the application of these time-series analysis methods to large data sets, it is necessary to turn to high-performance computing (HPC) resources and software designs. This article presents an overview of the development of the HiTempo platform, which was designed to facilitate research into time-series analysis of hyper-temporal sequences of satellite image data. The platform is designed to facilitate the exhaustive evaluation and comparison of algorithms, while ensuring that experiments are reproducible. Early results obtained using applications built within the platform are presented. A sample model-based change detection algorithm based on the extended Kalman filter has been shown to achieve a 97% detection success rate on simulated data sets constructed fromMODIS time series. This algorithm has also been parallelized to illustrate that an entire sequence of MODIS tiles (415 tiles over 9 years) can be processed in under 19 minutes using 32 processors. DA - 2012-08 DB - ResearchSpace DP - CSIR KW - Kalman filters KW - Earth observation satellites KW - Data sets analysis KW - Remote-sensing satellite data KW - MODIS time series LK - https://researchspace.csir.co.za PY - 2012 SM - 0143-1161 T1 - HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment TI - HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment UR - http://hdl.handle.net/10204/6575 ER -