High-Performance Processing Of Massive Remote Sensing Data And Its Visualization Application

Development of high-resolution earth observation technology was16major projects for science and technology development established in2006in China, and the implementation of this project will promote the development of its application technology. A very effective way to fulfill this project is using some techniques such as distributed storage, high-performance cluster etc, but these techniques bring issues such as cost, energy consumption. Recent years, GPU-based general-purpose computing technology bring us another way to resolve the problem of large scale computing. In conditions of moderm GPU technology, using general-purpose GPU computing interface can provide acceleration for image processing efficiency, and it can provid at least several times higher performance than mormal computer systems by using hybrid GPU+CPU computing systems, even hundreds of times, thousands of times more.In addition, spatial information’s digital storage, transmission through network, visualizations and intelligent application is an inevitable trend, it will bring us new challenges. For good use these massive remote sensing data, there are many problems to be solved such as network transmission optimization, storage methods, application model etc, and these also are the key issues discussed in this article.This paper’s main content as follows:1) Storage model and scheduling of massive remote sensing dataTo meet the requirements of data processing system and visualization applications of remote sensing data, proposed a mixed-mode data management system, and achieved satisfactory results in practical applications..2) High-performance processing and analysis of remote sensing dataThis part is the focus of this article content. Combined with the actual characteristics of the remote sensing data processing algorithms, this part discusses the technique that using GPU acceleration technology to optimize image processing algorithm. Emphasis on surface correlation algorithm, as well as the analysis algorithm for vector and raster data in a wide range conditions, explained the convolution, image mathching, vector arithmetic, vector and raster joint analysis and global statistics process in detail.3) Massive remote sensing data visualization applicationIn this section, The author highlights the technology of3D terrain rendering and its interactive method for massive remote sensing data. Focuses on the conditions of vary index in the database of the DEM and the orthophoto, how to render them correct without coordinate registration. Also used the technique of programmable GPU, the experiment achieved satisfactory results in both of efficiency and effectiveness. Specifically in the complex visual effects applications, it is very simple than the traditional opengl way in implementation.The technique discussed in this paper is very important to the solutions for the management and application system based on massive remote sensing data visualization, and has been successful applied in a data processing system in cluster environment. Using the general-purpose GPU technology, some raster processing algorithms had achieved ten or several dozen times efficiency increased compared to the user’s original way.