Geo Spatial Image Retrieval Using Content- Based Image Retrieval Technique

--Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based Geospatial Image Retrieval includes Content-based image retrieval Technique (CBIR). CBIR use the Quadratic Distance and the Integrated Region Matching (IRM) methods. The Quadratic Distance method, though yields metric distance, is computationally expensive. The IRM novel similarity measure for region-based image similarity comparison that gives optimal solution. The targeted image retrieval systems represent an image by a set of regions, roughly corresponding to objects, which are characterized by features reciting colour, texture, shape, and location properties. The IRM measure for evaluating overall similarity between images incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based on individual regions, the overall similarity approach reduces the innocence of inaccurate segmentation, helps to clarify the semantics of a particular region, and enables a simple querying interface for regionbased image retrieval systems and after finding some feasible set of images using IRM, this system considers one of the image from the set as input and determines unique image as optimal solution.. Our system in general achieves more accurate retrieval at higher speed.