Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/rem...Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/remote sensing(GIS/RS) researchers rent more public clouds or establish more private clouds.However,a large proportion of these clouds are found to be underutilized,since users do not deal with big data every day.The low usage of cloud resources violates the original intention of cloud computing,which is to save resources by improving usage.In this work,a low-cost cloud computing solution was proposed for geo-information processing,especially for temporary processing tasks.The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory.The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study.Compared to commercial public clouds and dedicated private clouds,the proposed solution is more low-cost and resource-saving,and is more suitable for GIS/RS applications.展开更多
Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process...Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture radars.It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the kernel.Different from the traditional CS algorithm processing,the system divides data processing into three stages.The computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each stage.The method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing time.Gaofen-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.展开更多
基金Project(41401434)supported by the National Natural Science Foundation of China
文摘Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/remote sensing(GIS/RS) researchers rent more public clouds or establish more private clouds.However,a large proportion of these clouds are found to be underutilized,since users do not deal with big data every day.The low usage of cloud resources violates the original intention of cloud computing,which is to save resources by improving usage.In this work,a low-cost cloud computing solution was proposed for geo-information processing,especially for temporary processing tasks.The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory.The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study.Compared to commercial public clouds and dedicated private clouds,the proposed solution is more low-cost and resource-saving,and is more suitable for GIS/RS applications.
基金Project(2017YFC1405600)supported by the National Key R&D Program of ChinaProject(18JK05032)supported by the Scientific Research Project of Education Department of Shaanxi Province,China。
文摘Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture radars.It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the kernel.Different from the traditional CS algorithm processing,the system divides data processing into three stages.The computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each stage.The method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing time.Gaofen-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.
