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Also,surfaces constitutean importantlayer ofany GIS database. Abstract Withthegrowingavailabilityofhigh-resolutiondigitalcameras, Automatic matching of free-form linear features in overlapping the need for automatic and reliable surface reconstruction from large-scale imagery over urban areas still remains to be a imagery is becoming urgent. Automatic surface reconstruction problem in both the photogrammetric and computer vision from large-scale imagery over urban areas remains an unsolved communities. Although there is a variety of algorithms that problem in spite of the many efforts in the photogrammetric have been developed to solve this problem, reliable results are and computer vision fields. The complexity of the input imag- not always guaranteed. Differences in illumination conditions, ery and the ill-posed characteristics of that problem are among relief displacement, and occlusions are some of the factors themajor obstaclesencountered byresearchers. Thetraditional that make solving the matching problem more challenging. photogrammetric solution for surface reconstruction has three The deficiency of available techniques stems from the fact that basic steps. First, conjugate (matching) entities within overlap- they do not consider the perspective geometry of the imaging ping images are determined. The second step involves the system in the matching process. Moreover, it is usually determinationof therelativeorientation parameters(ROP)relat- assumed that conjugate entities are almost exact copies of ingthe twoimages ofastereo pair.Finally, matchedentitiesare each other (this is rarely the case). The need for a reliable projected into the stereo model using the derived ROP in step 2. algorithm that can handle large-scale imagery over urban Solving the correspondence problem is the most difficult step areas is growing, especially with the increasing availability within the surface reconstruction process. Strategies described of high-resolution aerial imagery. In this research, a new in the photogrammetric and computer vision literature for approach for automatic matching and three-dimensional finding conjugate entities within overlapping images include reconstruction of free-form linear features from stereo images area-based matching, feature-based matching, and relational is proposed. The suggested strategy is based on The Modified matching (Ackermann, 1984; Grimson, 1985; Rosenholm, Iterated Hough Transform (MIHT) for Robust Parameter 1987; Schenk, 1999). Estimation. MIHT relies on the mathematical relationship Automatic matching of distinct points is common in the between conjugate entities (the coplanarity condition when photogrammetric community. It usually starts by searching for dealing with a stereo pair). As a result, it overcomes problems interestingpoints thatsatisfy distinctness,stability,invariance, arising from relief displacements and/or occlusions. Moreover, uniqueness, and interpretability criteria. An extensive body of |
