Technologies Of Storage And Efficient Management On Cloud Computing For High Resolution Remote Sensing Image

With the development of earth observation technologies, integrated earth observation system of remote sensing satellites and ground systems has been established preliminary in China, and numerous high resolution remote sensing image data has been accumulated by various industries and research institution. However, the "Information silos" caused by dispersed storage and management, has seriously restricted the development of high resolution remote sensing image data sharing and application. The issues of how to integrate and manage the high resolution remote sensing image resources, provide technical support for high resolution remote sensing image sharing services and high performance application services in distributed computing environment, is urgently to be solved. In recent years, the development of cloud computing technology provides a new effective method for rapid accessing and efficient processing of high resolution remote sensing image.Based on the analysis of characteristics and applications of high resolution remote sensing images, combining cloud computing technology, which including virtualization, distributed storage, and distributed computing; the paper designed a model of high resolution remote sensing image storage, and then designed a management platform and some high-performance computing services. Finally, a prototype experimental system was developed by combined land plan services with this platform. The main research works are as follows:1. Designed a high resolution remote sensing image storage model C-RSM on cloud computing:by analyzing and comparing current mainstream cloud computing platforms, and focusing on characteristics of remote sensing image data sharing and map service etc., we designed high resolution remote sensing image organization method on Hadoop, and spatiotemporal management mechanism; then proposed storage strategy of high resolution remote sensing image data on Hadoop, and designed an algorithm of storing-accessing high resolution remote sensing image on Hadoop.2. Designed a management platform of high resolution remote sensing image based on C-RSM, which is called C-RSMP:after analyzing and comparing the spatial data management technology on current distributed computing environment, we summarized the advantages of using cloud computing technology to managing high resolution remote sensing images, and we integrated two cloud computing platform (Hadoop and Eucalyptus) for managing spatial data, then by analyzing the deficiency of current cloud GIS development, we designed the C-RSMP's architecture structure, services structure, and designed C-RSMP's high resolution remote sensing image fundamental services, including sharing service, map service, and high performance computing services of high resolution remote sensing image data in cloud computing environment. We proposed an approach that combined the parallel computing capability of GPU to cloud computing for the first time. Based on designing the algorithms of resampling and building pyramid model, which are accelerating by GPU, we redesigned these algorithms on Hadoop. The high performance computing services included distributed tasks based on Eucalyptus virtual resources management and high performance computing based on MapReduce.3. Achieve the prototype experimental system of land plan services on C_RSMP: firstly we discussed the system architecture and system deployment in cloud computing environment, then demonstrated the system's main modules, including land plan affairs, cloud computing platform management, sharing service, map service, and high performance computing service of high resolution remote sensing image data. Finally, we mainly tested the key algorithms of storing-accessing service, map service, high performance computing service of high resolution remote sensing image, by using land plan data of several parts in Zhejiang province. The results of experiments proved the high performance of our platform.The results showed that our research works can solve the problem on distributed storage and management of remote sensing image. The proposed high-performance computing services can be referenced for other serial algorithms migrating to our platform. Our research work on combining C-RSMP and land plan industry can also be applied to other industries to migrate to the cloud computing environment.