Research On Integrative Storage And High-Performance Processing Of Remote Sensing Data In SIG

At present, the quantity of spatial data (especially remote sensing data) is growing very rapidly in both military applications and common applications, and the enormous distributed spatial data is hard to be managed effectively. On the other hand, the remote sensing data processing is so complex that it proposes greater demands on computing capability to implement large-scale, high-resolution, and high-quality analysis and processing of remote sensing data.The Spatial Information Grid (SIG) is a novel spatial information infrastructure. SIG integrates some advanced techniques, such as Web Services, Grid Computing etc., to aggregate and share enormous distributed spatial information resources and provide the powerful abilities of processing and information services. In the technical architecture of SIG, the problems of distributed spatial information sharing, high-performance cooperative processing etc., are need to be studied and solved firstly. As one kind of important spatial data types in the SIG, remote sensing data is facing the same applied problems above, so it is necessary to introduce and apply some new techniques and methods to meet the new applied demands.According to the requirements of large-scale data processing and enormous data storage in SIG, this thesis set remote sensing data as data source, take SIG as system framework, and put research emphasis on integrative storage and high-performance processing based on grid computing technology. Firstly, we analyze system structures and technical characteristics of SIG, illustrate its research background. Then we study the techniques of storage, organizing and management of the remote sensing data. Furthermore, based on analyzing the Globus system, we study some key techniques of fast remote sensing data processing, including basic computing environment (Condor and PBS), mechanism of resources discovering, and algorithms of grid resource scheduling. Finally, an experiment system is implemented to aggregate several heterogeneous computing resources to form an integrative application environment for job submitting, distributing and executing. Hence, large-scale computing resources sharing and cooperating is implemented in SIG, and the efficiency of data processing and the computing is improved. This experiment system is validated and applied in national'863'project, i.e.'SIG System Platform for Representative Spatial Information Applications'.