METHODOLOGY AND TOOLS FOR ALIGNING VECTOR AND IMAGE DATA

This research addresses the rising critical need for automated conflation of vector/raster data as more and more high resolution data are collected for updates/improvement of graphic products employed in various mission areas of the Army and other user communities. Our conflation methodology derives from the integration of two well-established conflation approaches, and is developed with a preliminary software design in Phase I. Successful testing using Government data will be followed with a Phase II effort wherein we will complete the conflation system design and produce a prototype ready for deployment as a commercial product. Our approach starts with a deep scientific understanding of the complexities/challenges that characterize the conflation problem. We attack the misalignment issues of nonlinearity, disparate scales, uncontrolled noise, etc., on a first principles basis using rigorous mathematics rather than empirical trail and error adjustments that basically distribute the misalignment rather than attack fundamental causes. In developing our integrated methodology, we will establish the synergism achieved by combining two approaches: use of algebraic algorithms and similarity transformation of local features. We establish three fundamental research hypotheses about these complementing approaches, which will be tested and evaluated in order to provide a technical roadmap toward the optimal conflation solution.