Geospatial image information management



FUTURE OF INTEGRATED INFORMATION MANAGEMENT OF RS IMAGES
The new design concept for on-board geo-data management in future intelligent earth observing satellite is as under.

On-board Geo-database management in future RS satellites
The integration of satellite raster image data, with the already existing geo data, is one of the important challenges of image processing, image geo-coding, image data management. The traditional GIS holds the key for integration of satellite imagery, on space-borne and ground controlled and mirrored GIS platform. There are three basic models for space borne integration (a) Separated but Parallel Integration ; which means that the image processing system and GIS system are separate but are used in integrated fashion to transfer knowledge based geo-spatial information between the two data handling systems where GIS compliments the imagery database with knowledge base, (b) Seamless Integration; this means the GIS and image analysis system are stored together and the functions of both the system are interfaced and accessed through a common interface and (c) Total Integration; this means the RS and geo data support each other for analysis and processing and make full use of benefits of image analysis functionality and GIS simultaneously. The future earth observation satellite are likely to have on-board data integration functionality and will be able to manage minimum two data sets namely from geo data and DEM (vector and raster) and will also seamlessly link them together. To achieve this following data type and its modeling will be required.

Data types and data modeling
DTM data: The DTM data will provide the elevation information to the end user and will also traditionally provide data for carrying out ortho image rectification so as to link with ortho - RS database system existing on earth user. The data will be stored in the form of typical regular raster data, triangular irregular network (TIN) and hybrid data. The DTM database will also be provided as attribute associated with the imagery. This data set will also be used for programming the satellite downloads.

Satellite image data: As a practice the satellite image from the on-board sensor are co-registered with the ground co-ordinate system using on-board image processor with specific algorithms. However, in the advent of on board data management, only the change in coordinate data will be transmitted resulting in transmission of the changed area image data. To achieve high performance image filters and modulators with refined contrast matching techniques, correlation function and other contemporary technologies will be utilized.

Spatial data: The geo spatial data is an abstract entity in the real world. The spatial data has two obvious features, viz., geometric and physical characteristics identifiable by point object, line object, area object and complex objects. This database will hold the feature characteristics of the objects updated time to time from the earth station or with the privileged clients. This database will have all characteristics of typical GIS.

Client knowledge base engine: This database engine provides the client knowledge base interface and keep a track of all the data queried and in process so that the speedy scheduling of the data is achieved, by switching off and on the filter circuits and modulators so as to achieve maximum of the processing need. This engine will also have standard work process scheduler for most of the digital image processing.

Implementation of on-board data management systems
Work flow modelling: The work force modeling will be the key process controller as it functions as interface to execute query modeling in the technical functions to the human resource management in the ground based system.

DTM data management system: The purpose of a global DEM database on-board is to directly provide elevation information to user without any processing on ground. The DEM management will be carried out in block indexing fashion with an unique DEM identifier. The DEM processor will automatically generate DEM pyramid data for data management and will under take ortho-corrections.

Geo- spatial management system: This database system will hold the attribute data connected to spatial database connected by a unique identification number (geodetic co-ordinates). This will be achieved by the help of organizing attribute and spatial data in the same record of database or separating them and specifying the link. This scheme will directly employ the spatial data to a unique identification number (geodetic co-ordinates) so as to connect the attribute and spatial data.


Fig 5 Work flow of integrated management system

Processing and client knowledge data mining management system: Three types of data as above are stored in three separate databases. An integrated management system is required to manage and process three sub systems and query database. A unique dynamic identification number or a pointer will connect all the databases and generate the dynamic query. A integrated management system will also be responsible for imposing scale compression process, co-ordinate transformation and transmission to on-board data coordinator and distributor.

Key technologies for the on-board integrated information management: The management system uses the image as a reference layer in which the geo-data and the DEM data is imposed for analyzing and querying in the real world environment. This type of product contains more information than the traditional real world information database generated from satellite. User can better orient themselves with geo-image maps spatially when they are not aware of image processing work process needed for undertaking the job. The realization of this function will change the imagery storage concept and requirements for the similar images will reduce considerably. There are some key thrust areas that need to be developed to make this technology successful.
  • Retrieving the raster images, DEM and attributes as well as returning them to user bandwidth technology is the key technology faced for more advanced querying and that too particularly when it is on the fly.
  • As the complexity increases more data especially high-resolution satellite data will be demanded for task. It will be a time consuming to query them at and simultaneously to take decision and determine which data holds priority. Hence the key technology development area will be in parallel fast processing on-board satellite.
  • As mentioned the images will be orthorectified and will use geographic reference layer. The image will be in all three phases, i.e., differentially rectify, partly rectify and un rectify because of on-board processing speed. When the attribute data will be imposed on them the error are likely to be generated. The key technology area will be to on fly calculate the errors before transmitting the data to the client.
  • The universal problem of inappropriate linkage in DEM images and geo-data will produce confusion and inaccurate result in downloaded imagery. The key technology area is to develop an artificial intelligence to rectify the same.
  • The inconsistent work processes for the on-board processing will lead to inconsistent results. The key technology area will be to neural networks the artificial intelligence to provide solution from the heterogeneous queries.
CONCLUSION
The combination of RS and modern database technology is making possible the management of huge amount of RS image data. In addition, the RS image management not only significantly increases the system efficiencies, scalability, security and integrity it also extends the application of space of the RS image. Moreover, a RS data base system in future will provide a platform for on-board integration of satellite data with corresponding geo-data (spatial and attributable) and DEM data in the satellite, reducing the work planning, processing and acquisition delays.

Acknowledgement & References
The works carried out by Mihai Datcu , Herbert Daschiel et al. Yufei Wang , Chris Rizos , Linlin Ge & Craig Roberts from school of surveying Sydney. Paul Kaufrmann and Guoqing Zhou from old Dominium University. Works on knowledge based interpretation by Stefan Groves et al, various proceedings of IEEE , Data from various conferences, Journals and references from open source have contributed towards the development of this article. I thank Brigadier RC Padhi for his guidance and support provided for this work.

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