The Massive Remote Sensing Data Storage And Applied Research

Due to the continuous progress of remote sensing observation technology and the application of remote sensing metadata and remote sensing image data in various fields of society,remote sensing observation data of different standards and structures are emerging widely.The remote sensing databases established by various scientific research institutions and government departments are aimed at the respective industries,the standards are not unified,the database is incompatible,and is not conducive to the sharing and application of remote sensing data between departments,nor can it be good to solve the efficient access problem of remote sensing data resulting from the development of remote sensing observation technology.In order to better access the massive amount of remote sensing metadata and remote sensing image data,this paper designs and implements a storage system efficient storage remote sensing data storage system,the system uses the front edge data storage technology,the system bottom uses the MongoDB database developed by 10 g company to realize the high efficient storage of standard remote sensing metadata,and uses grid to realize the high efficient storage of remote sensing image files.This paper introduces the architecture of the system,the implementation principle,the related technology,and describes in detail a method of efficient storage based on mapping template designed for the characteristics of remote sensing metadata entities describing attribute standards.At present,many cities are suffering from severe floods and floods,due to the heavy rain or continuous precipitation over urban drainage ability which produce the phenomenon of water disaster in the city.With the speeding up of urbanization in our country,the change of urban ecosystem,urban sudden disasters not only failed to reduce flooding in nearly two years,but increased year by year,more and more influence,has become the urban development faces severe challenges.In the past,it was limited by technical level,unable to make effective advance prevention of urban rainstorms and waterlogging disasters,and only after the event of disaster recovery.In order to avoid the city flood caused serious disasters,we should first find out all the low-lying city points,and then analyze these low-lying point row level,for some waterlogged and processing points were monitored.This paper exposes a massive remote sensing data and Hadoop based search for city low-lying point method,the method including the remote sensing image is converted into Hadoop program enter the specific format of DEM data,and then through the Hadoop distributed parallel computing of the DEM data in order to find out the urban areas,all low points.