Blunder Elimination Techniques in Adaptive Automatic Terrain Extraction

This paper introduced a new automatic terrain extraction (ATE) module inside ERDAS’s photogrammetric software LPS. This method uses a global DEM to initialize a surface model and iteratively refines it with image registration on different pyramid levels. Search range used in image registration is adaptively controlled by elevation range of matched feature points. However, mismatches and elevation blunders may cause search range to be out-of-scope and fail ATE process. We used three blunder elimination techniques to ensure the convergence of search range: positional cross-correlation, PCA-based blunder elimination, and object filtering. The method is tested with images from various sensors including frame cameras, satellites, and Leica’s ADS40.