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Studies on RS Image’s Geo-Information Issue System
Storage and Organization of Multi-Source Image Data
Considering the difficulties of storing the multi-resolution, multi-band and multi-epoch image data using RDBMS, this paper adopts the solid index mechanism of “pyramid, block, layer, section” and logic database to store and manage image data using RDBMS. This can take full advantage of the mature techniques of RDBMS. Solid Index Mechanism of “Pyramid, Block, Layer, Section” Storage of RS image data must consider the characters of its multi-resolution, multi-band, multi-epoch. According to the solid index mechanism of “pyramid, block, layer, section”, a solid spatial database based on spatial relations and image data characters is constructed. The organization pattern based on “pyramid, block, layer, section” is as the following figure 3 shows. 
‘Pyramid’ represents image spatial resolution, and different pyramid layer represents different spatial resolution. The top of pyramid represents the minimum spatial resolution. ‘Block’ represents different region of image, i.e. the different geographical region in reality. ‘Layer’ represents optic resolution, i.e. the band of image. ‘Section’ represents different time of image. Image data of a same locale from the same band of a sensor could be different because of collecting time. ‘Pyramid’ organization can organize image data in many scales and optimize data transmission according to different requirement of different users. ‘Block’ organization helps system locate the corresponding region of data files quickly according to requirement decreasing accessing time and data quantity. ‘Layer’ and ‘Section’ organization of image data retains the RS data’s peculiar properties- optic resolution and temporal character. When user queries image data, system provides some optional choices to give different time, different band image data. Logic Database The solid index of ‘pyramid, block, layer, section’ organizes multi-image data and constructs index database of image data. So there is a logical relation between this database and real image file. Compared with image files, this database is abstract, virtual and logical but it can be managed with the current mature RDBMS. The logic database was constructed according to the solid index mechanism and query items provided to the clients. A theme refers to peculiar image data from a certain sensor. Different themes are stored as different tables in a RDBMS. Different bands from a same sensor are also stored at different layers just contain a same index field indicating the same source. The image data from same senor, same band but different time are also stored at different tables. This table structure of logic database can provide original information query according to RS image data peculiarities, meanwhile, it is avoid of a large table affecting response speed. Considering original files’ large data, it is necessary to cut original file into regular tiles. When clients query image data, server side provides some corresponding tiles. Before image data is put into logic database, it is cut into many tiles according to its pixels size, and then these tiles are stored at an individual database. These tiles are not real image data, they are the logical tiles which indicating the corresponding part of real image. In fact, server side looks up the corresponding image files and reads partial image data according to the record of logical tiles in database. Pattern’s Application According to above image data management pattern adopting solid index of ‘pyramid, block, layer, section’ and logic database, a distributed parallel image database system (PARIDS) is constructed to provide storage, management and query of large image data. In the application of system, we adopted ETM six bands of 1, 2, 3, 4, 5, 7, 8, the panchromatic band of spot and ten bands of MODIS as experimental data. The whole system includes Geo-information extracting of RS image, construction of logic database, storage and setting up logic relationship of system logic tables. In the design, the application system was divided into 3 layers, they are physical layer, logic layer and application layer. The architecture and function flow of the system are showed in figure 4.  Application layer is the interface between system and users. The communication between them is carried in browser through http protocol. The parallel background severs process parameters submitted to web servers and deploy the logic tables. Logic layer includes logic database constructed by the solid index mechanism of ‘Pyramid, Block, Layer, Epoch’ and it is a getaway between application layer and physical layer. It is jointed to application layer by WEB server, and it is jointed to physical layer by some image processing procedures and arithmetic’s. These procedures are deployed after WEB server has deployed logic database. Physical layer has stored large volume image data, and every image data file was classified by the solid index of ‘Pyramid, Block, Layer and Epoch’. Subsequently a strict file catalog was constructed according to the file system, which was organized by system itself. Conclusions This paper studies distributed parallel storage, management and query of RS image data using current RDBMS based on solid index of ‘pyramid, block, layer, section’ of image data and logic database. We construct a pattern and apply it to the design of parallel image database system (PARIDS) illustrating that the pattern is viable. This pattern is constructed at parallel platform JIAJIA. JIAJIA is also a background sever clusters which improving server’ performance. A graft of this pattern from parallel platform to a stand-alone sever should be applicable in theory just sacrificing the performance of sever side. Considering the rapid improvement of hardware, this study will be more meaningful. The parallel platform enhances the speed and efficiency of background server. So, increasing some process functions at client side will exhibit system’s superiority much more. References
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