The automation of extracting planner surfaces is a main field of research in digital photogrammetry. These surfaces are essential to generate three dimensional GIS databases. Surfaces are usually determined from either DEMs or images. Each dataset provides a different type of information. Thus, the combination of the two datasets should enhance the surface reconstruction process. This paper presents a new technique for generating 3D surfaces by combining both correlation-based DEMs and aerial images. The process starts by discriminating DEM points that represent planner surfaces using local statistics of neighboring elevations and intensities and point elevations. A segmented orthophoto is then used to group these points into different regions. The elevations of the points in each region are fed into a least squares adjustment model to compute the best-fit planner surface parameters. Refinement of surface borders is then performed using a region growing algorithm. The RMSE for five test sites showed a spatial accuracy of 5 - 8 cm.
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