Design method for realizing Hbase database remote sensing big data storage model based on Google S2

Abstract

The invention discloses a design method for realizing an Hbase database remote sensing big data storage model based on Google S2. The method comprisesa; achieving grid cutting of remote sensing data through a Google S2 algorithm, partitioning, fragmentation and ground space discretization are conducted on a large-range whole remote sensing image, and storage management of image data is facilitated; through establishment of a table storage model of the Hbase database, attribute expression of the partition remote sensing image in multiple dimensions is achieved, and data structure integration ofmulti-source heterogeneous remote sensing image data is achieved in a data band discrete storage mode. Characteristics of different types of remote sensing image data are fully considered, efficientstorage of remote sensing big data is achieved, requirements of users under different application scenarios are met, and expandability and data balance of the system are effectively considered.

Description

technical field [0001] The invention relates to the technical field of database management, in particular to a design method for implementing a Hbase database remote sensing big data storage model based on Google S2. Background technique [0002] Remote sensing data has the characteristics of massive, multi-source, heterogeneous, and distributed storage. The storage and management methods of remote sensing data mainly include database management, file-based management, and mixed file-database management. The development of remote sensing image storage and management systems at home and abroad is constantly emerging. For example, the multi-resolution seamless image database system Geo-ImageDB developed by Wuhan University uses file management to store and manage multi-scale remote sensing image data; the Canadian Center for Remote Sensing Images (CCRS) established The remote sensing image database uses the database to store the relevant metadata information of satellite image...

AI Technical Summary

Problems solved by technology

In the traditional remote sensing image database management method based on file management and a mixture of files and relational databases, remote sensing images exist in the form of files, which is not conducive to the management and distribution of image data, and the security of the system is very low.

[0003] At the same time, there are different ways to obtain remote sensing images, and the data formats of remote sensing images are also various. Most remote sensing image management systems based on databases do not have a unified standard for storing various types of data, and their formats, types, and numbers vary. Undoubtedly increase the system development cost and construction cycle

[0004] In the face of the development of massive data, the fine management of remote sensing data management has become particularly important, and the traditional data management method in the form of metadata + image files has become increasingly inapplicable in the face of massive data

The highlighted problems include: the data provision method is too single, and the data management accuracy is not enough; the same data, multiple users need to perform the same preprocessing operation, and cannot be effectively shared; there is a large amount of invalid information in one data transmission, which wastes Bandwidth resources; the same type of satellite data across the satellite system cannot be used on the same layer; the corresponding spatial areas where the information cannot be directly restored after data analysis need to build another database for management

Images