Conflation of images with algebraic structures

Spatial decision making and analysis heavily depend on quality of image registration and conflation. An approach to conflation/registration of images that does not depend on identifying common points is being developed. It uses the method of algebraic invariants to provide a common set of coordinates to images using chains of line segments formally described as polylines. It is shown the invariant algebraic properties of the polylines provide sufficient information to automate conflation. When there are discrepancies between the image data sets, robust measures of the possibility and quality of match (measures of correctness) are necessary. Such measures are offered based on image structural characteristics. These measures may also be used to mitigate the effects of sensor and observational artifacts. This new approach grew from a careful review of conflating processes based on computational topology and geometry. This chapter describes the theory of algebraic invariants, a conflation/ registration method with measures of correctness of feature matching.

Key words: data fusion, imagery conflation, algebraic invariants, geospatial feature, polyline match, measure of correctness, structural similarity, structural interpolation