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REFINEMENT OF DIGITAL ELEVATION MODELS IN URBAN AREAS USING BREAKLINES VIA A MULTI-PHOTO LEAST-SQUARES MATCHING ALGORITHM

High quality digital elevation models (DEMs) are essential for several applications in urban areas such as telecommunication, urban development, and city planning and management. Sudden changes in the Earth's surface weaken existing automatic terrain extraction (ATE) techniques from providing accurate and reliable terrain representation; therefore, a refining step is necessary for automatically extracted DEMs. DEM refinement is carried out either manually or automatically. Manual refinement is costly and time consuming; consequently, automatic algorithms are preferred. In this research, a new algorithm is proposed to enhance the accuracy of DEMs in urban areas. The algorithm was based on implementing a least-squares estimation model using both breaklines and image signals, incorporated through least-squares matching. Errors in conventional least-squares matching are caused by incorrect modeling at terrain discontinuities when ground objects are represented by continuous models. Accordingly, breaklines were utilized in this research to separate DEM posts belonging to different surfaces. Moreover, pixels belonging to obscured features were excluded from the least-squares matching. The algorithm was tested with four one-meter post spacing DEMs generated from 1:4000 aerial photographs scanned at 30 μm. The average RMS for four test sites was reduced from ten to five centimeters. This demonstrates the benefits of the proposed algorithm in minimizing the effects of sudden changes on the accuracy of DEMs in urban areas.