RESEARCH ON AUTOMATED DATA PROCESSING METHOD OF PCI GEOGRAPHIC IMAGING ACCELERATOR (GXL) SYSTEM FOR BIG DATA OF SUPERVIEW-1 REMOTE SENSING IMAGE
- 1National Quality Inspection and Testing Center for Surveying and Mapping Products, Beijing, China
- 2SpaceWill Info. Co., Ltd, Beijing, China
Keywords: Remote Sensing, Big Data, Natural Resources, Superview-1 Satellite Constellation, GXL System, Data Processing
Abstract. The Chinese government attaches great importance to the development of remote sensing satellites and has now formed satellite series such as meteorological satellites, ocean satellites, resource satellites and environmental disaster reduction satellites. At the same time, many commercial space programs have been put forward or implemented in China in recent years. As the first commercial-oriented multi-means and high-resolution optical remote sensing satellite constellation in China, "Superview-1" satellite constellation is undoubtedly one of the outstanding representatives. In the implementation of digital orthophoto map project, how to deal with large data of remote sensing image in large area quickly and accurately is a difficult problem we are facing to be solved urgently. PCI geographic imaging accelerator (GXL) system plays an important role in the production of digital orthophoto image by virtue of its advantages in mass image processing. This paper briefly introduces the structure and advantages of "Superview-1" satellite constellation, introduces in detail the production process and the key technologies of "Superview-1" satellite constellation image by GXL system, and carries out research on computational efficiency comparison and production test by GXL system. In this paper, the advantages of this software system in the production of tremendous amount of satellite remote sensing image data are analyzed. At the same time, in the existing engineering practice, due to the limitation of current data scale and production mode, the software system still has some obscure shortcomings. Under the background of natural resources, the amount of remote sensing data will continue to increase substantially in the future. By analyzing these shortcomings, this paper combines the traditional production mode with the background of remote sensing data in the new era, and puts forward some reasonable and useful suggestions.