Error estimation techniques to refine overlapping aerial image mosaic processes via detected parameters
In this paper, I propose to demonstrate a means of error estimation preprocessing in the assembly of overlapping aerial image mosaics. The mosaic program automatically assembles several hundred aerial images from a data set by aligning them, via image registration using a pattern search method, onto a GIS grid. The method presented first locates the images from a data set that it predicts will not align well via the mosaic process, then it uses a correlation function, optimized by a modified Hooke and Jeeves algorithm, to provide a more optimal transformation function input to the mosaic program. Using this improved input, the mosaic program will generate mosaics whose constituent images are better aligned. This dissertation will demonstrate that creating more area based regions for alignment within the images, filtering them for disqualifying parameters, and using the good ones to optimize the above transformation input will significantly improve the quality of mosaics produced by the mosaic program by improving the alignment of strategically selected, difficult images.