Study on subdivision index model for scene-based RS data
遥感分景数据的剖分索引模型

With the rapid development of space technology and earth observation technology,modern remote sensing technology has been able to provide worldwide observed data collected in many means in a dynamic,fast and accurate manner.In this stage,remote sensing data increase rapidly accordingly,and how to manage and retrieve these data efficiently has become a major issue in both academic and application areas.Global Subdivision System opens up new avenues for solution to this problem.This peper studies the characteristics of scene-based remote sensing data and the features of the EMD Global Subdivision Model,and then in regard to the disadvantages of the existing index methods for remote sensing data,three index models are put forward based on the EMD Global Subdivision Model:an index model based on the mapping relationships of images and meshes,an index model based on EMD data model and an index model for global subdivision data identifiers.These three index models explore the solution to the issue in three aspects.And an application example is given to create the index of linear objects and planar objects by use of the scene-based RS data subdivision index model.This study mainly addresses the following aspects.(1)This research studies the characteristics of scene-based remote sensing data and the features of the EMD Global Subdivision Model,as well as the mapping relationships and corresponding criteria between them.(2)In regard to the lack of space-time recording capabilities in the current storage and index methods for remote sensing data,an index model based on the mapping relationships of images and meshes is put forward.(3)In regard to the lack of support for multi-scale data of the current index models for remote sensing data,an index model based on EMD data model is established.(4) In regard to the lack of a unified index framework for multi-source remote sensing data,an index model for global subdivision data identifiers is put forward.(5)How to get remote sensing images from a geographic object index is studied.