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Geospatial image information management KEY TECHNOLOGIES IN BUILDING RS IMAGE INFORMATION DATABASE The RS database is different from traditional image database of telecommunications, human resource, energy, police, agriculture, forestry, land development and to some extent geo-information. Though these databases contribute towards the knowledge base data extraction, it supports and strengthens the achievements of many social and environmental objectives, to be achieved by the RS image database also. The concept of spatial data infrastructure (SDI) is an integration of components technologies, policies, standardization and human work-process (resources) necessary to acquire, process, store, distribute and improve the utilization of geo spatial data to the widest possible group of users. With rapid advancements in the field of RS technology, a huge amount of RS database and knowledge-base can be made available seamlessly and near-instantaneously to varied kinds of users simultaneously. The concepts are changing and gaining momentum towards Ortho-RS image spatial framework. Hence, the development of RS images information management system must be able to handle vast volumes of data. The key technology for achieving this lies with:  Fig 1 Integration of image data fusion in 3D environment for knowledge information base Virtual seamless mosaic The virtual mosaic is the computer-defined mosaic of the images to make 'seamless mosaic'. It maintains the integrity of RS image database management system, that includes spatial, spectral and radiometric though it is independent of geometric corrections. The data used in the virtual mosaic of the RS data needs to be geometrically corrected to compensate for the distortions by earth curvature, panoramic distortions, relief displacement and atmospheric refraction so as to be re-projected to a common datum as spheroid and projections and finally drape them with the DTM. The database thus made after these functions is mainly continuous and have elevation information data though the data content is largely devoid of value added information. However, the data structure holds good in feature based co-relation mechanics and are widely used for the knowledge content based data mining and management.  Fig 2 Images hierarchical structure in database  Fig 3 Geo-spatial query model  Fig 4 On-board data management model Data fusion of Multi source images The RS images of the earth atmosphere, hydrosphere and geosphere are continuously created from various platforms and sensors. Hence, the fusion of multi-platform, multi-resolution, multi-temporality, multi-sensor, multi-angle, and multi spectral image information is an obvious strategy. In order to enrich our future classification in area of interest (AOI), and analytical image based knowledge base, such solution strategy will increase the reliability and accuracy of RS images. There are three methodologies of RS data fusion and it's handling (a) Pixel based, (b) Feature based and (c) Determination based. Hierarchical management Multi-resolution is a very important and central aspect of RS image management systems, because different users need different levels of RS image details (Hierarchical Organisation). A pyramidal structure is the most common approach for multi resolution or hierarchical management system. In this each of the derived layers stack on the top of previous layer. Various concepts of stacking have developed over the time for the coding of entire earth surface. Google Earth is a classical example of hierarchical management of images. Though the system is able to satisfy different requirements of resolution and minimizes the search time by balancing the load throughout the processor and network, the cost of the storage system is exorbitant. The benefits of each system have to be incorporated to build a comprehensive spatial data management systems. Spatial indexing and querying After the multi resolution management of RS imagery, cell indexing and searching is the most important issue for building RS image data base systems, because it directly impacts on the performance of the system. The work process and the human resource management is directly linked to the capabilities of indexing and querying of the system. Spatial index is another important index approach for spatial data structure. One of the most simple spatial query systems is explained. The system has been found to be very effective for moderately large scene based image database. Various knowledge base programme such as algorithms, which can find the probabilities of most visited images and scenes for workspace management, designer query geo-algorithms, metadata text search engine etc, can be incorporated along with the query models. Besides these there are ocean of technology in the data mining and geo-algorithms, which can undertake refined indexing and querying. |