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

The automatic determination of conjugate points in digital images, i.e. digital image matching, is one of the main topics in photogrammetric research. The reason is obvious, digital image matching helps to achieve a complete automation in photogrammetry. During the last few years a trend could be observed to perform digital image matching on a global rather than on a local scale and in object rather than in image space. This is done because isolated matching of small image windows can be subject to blunders, especially in areas of poor or repetitive image texture, or when the object surface shows discontinuities. As a result a general model for digital photogrammetry has been developed, 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 for the description of the object surface (e.g. the heights of a digital terrain model and the intensity values of all points on the surface), and the orientation parameters of the images. Any desired number of images, scanned in various spectral bands, can he processed simultaneously. The convergence radius or pull-in range, known to be rather poor (a few pixels only) in least squares matching, is considerably extended, and the computation time is considerably reduced by using a hierarchical procedure with image pyramids. Some tests using this approach on real aerial imagery were made. They constitute the first controlled tests of the approach and prove its applicability for practical needs.