A Grid Framework for Integration Multi-scale Vector and Imagery Data

With ever-growing archives of multi-source raster images and maps, many spatial applications such as multi-scale database updating, progressive web mapping and 3D terrain visualization call for rapidly automatic integration of GIS and imagery data. The object-oriented methodology display novel characteristics for multi-scale representation. While, management for multi-scale datasets is still lag behind, especially for multi-source data form different spatial reference system (DSRS). In this paper, we review problems with state of the art integration of multisource data. A hierarchical grid framework has been introduced, spatial information multi-grids (SIMG). Three fundamental components to do multi-scale and multi-source datasets analysis are required for SIMG. First, it is necessary to fastly unify different spatial reference system (DSRS) data. Secondly, efficient spatial grid and scale encoding must be applied to support flexible management of multi-scale datasets. Moreover, strategy delineated image data simplification from detailed to broad scale must to be developed. The approaches including the optimal scale identification, object-oriented upscaling and spatial grid and scale encoding for image-objects have been presented. And the experimental was implemented by applying the framework to integrate vector map of SRS in Beijing54 with Landsat TM image of SRS in WGS84, to detect city region sprawl in Zhengzhou located by the Yellow river, China. It is suggested by results that real-time DSRS integration need fewer time cost than traditional method. The image classification accuracy at optimal scale reached 90.4 percent of kappa, and upscaling results of multi-scale datasets here were more outstanding than multilevel wavelets method. So, this study was easily operated with great effectiveness