Global image matching and surface reconstruction in object space using three aerial images

Abstract

Global image matching is a general model for digital photogrammetry, integrating area-based multi-image matching, point determination, object surface reconstruction and orthoimage generation. Using this model the unknown quantities are estimated directly from the pixel intensity values and from control information in a nonlinear least squares adjustment. The unknown quantities are the geometric and radiometric parameters of the approximation of the object surface (e.g. the heights of a digital terrain model and the brightness values of each point on the surface), and the orientation parameters of the images. Some tests using this approach on real aerial imagery were made. A manually measured digital terrain model was used as reference. The main objective of the tests was to investigate if using more than two images in the global matching would increase the accuracy and reliability of the results. In this paper a review of the approach, the tests and the results are presented and evaluated. Some problems with using this approach on real imagery and practical applications are discussed, and suggestions for future development of the method are made.