14 September 2016 Integration and management of massive remote-sensing data based on GeoSOT subdivision model
Author Affiliations +
J. of Applied Remote Sensing, 10(3), 034003 (2016). doi:10.1117/1.JRS.10.034003
Owing to the rapid development of earth observation technology, the volume of spatial information is growing rapidly; therefore, improving query retrieval speed from large, rich data sources for remote-sensing data management systems is quite urgent. A global subdivision model, geographic coordinate subdivision grid with one-dimension integer coding on 2n-tree, which we propose as a solution, has been used in data management organizations. However, because a spatial object may cover several grids, ample data redundancy will occur when data are stored in relational databases. To solve this redundancy problem, we first combined the subdivision model with the spatial array database containing the inverted index. We proposed an improved approach for integrating and managing massive remote-sensing data. By adding a spatial code column in an array format in a database, spatial information in remote-sensing metadata can be stored and logically subdivided. We implemented our method in a Kingbase Enterprise Server database system and compared the results with the Oracle platform by simulating worldwide image data. Experimental results showed that our approach performed better than Oracle in terms of data integration and time and space efficiency. Our approach also offers an efficient storage management system for existing storage centers and management systems.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shuang Li, Chengqi Cheng, Bo Chen, Li Meng, "Integration and management of massive remote-sensing data based on GeoSOT subdivision model," Journal of Applied Remote Sensing 10(3), 034003 (14 September 2016). https://doi.org/10.1117/1.JRS.10.034003

Data modeling

Remote sensing


Data storage

Image storage

Image compression

Systems modeling


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