Research on Template Computing Mode of Remote Sensing Image Based on Partition Model

Abstract

As the amount of data rises and application needs expand, the efficient organization and management of remote sensing data has become a bottleneck restricting the application of remote sensing technology. The Global Partition Theory (GPT) and high performance computing provide an approach to solve the above mentioned problems. GPT studies how the Earth's surface is split into different levels of thickness seamless mesh and how to organize and manage it. Thus, rapid integration of mass remote sensing data of different sources, different types and different resolution can be achieved. Meanwhile, there is a natural segmentation of regional location and distributed storage features in spatial data in the partition organization framework, which makes remote sensing images computing model based on partition inherently parallel attributes. Combing a partition model of the Extended Model Based on Mapping Division (EMD), the researchers study the partition facet of remote sensing image, and propose the conceptual model and data model of partition facet template. Combing with parallel processing framework in high-performance computing of remote sensing image, the researchers design the template-based computing mode of partition facet and the partition process of spatial data. Through analyzing spatial relationship of partition facets, such as containment relationship, neighboring relationship and direction relationship, the researchers propose the basic calculation modes of partition template. There are aggregation, division in longitudinal and extend, conversion in transverse. This research paper is of great significance for expanding the application of GPT, improving the remote sensing technology speed, accelerating spatial information visualization analyzing and decision making speed. It also provides valuable guidance for studying high-performance remote sensing image processing in the future.