Translator Disclaimer
24 September 1993 Comparison of two terrain extraction algorithms: hierarchical relaxation correlation and global least squares matching
Author Affiliations +
Automated extraction of elevation data from stereo images requires automated images registration followed by photogrammetric mapping into a Digital Elevation Model (DEM). The Digital Production System (DPS) Data Extraction Segment (DE/S) of the Defense Mapping Agency (DMA) currently uses an image pyramid registration technique known as Hierarchical Relaxation Correlation (HRC) to perform Automated Terrain Extraction (ATE). Under an internal research and development project, GDE Systems has developed Global Least Squares Matching (GLSM) technique of nonlinear estimation requiring a simultaneous array algebra solution of a dense DEM as a part of the matching process. This paper focuses on traditional low density DEM production where the coarse-to-fine process of HRC and GLSM is stopped at lower image resolutions until the required DEM quality is reached. Tests were made comparing the HRC and GLSM results at various image resolutions against carefully edited and averaged check points of four cartographers from 1:40,000 and 1:80,000 softcopy stereo models. The results show that both HRC and GLSM far exceed the traditional mapping standard allowing an economic use of lower resolution source images. GLSM allowed up to five times lower image resolution than HRC producing acceptable contour plots with no manual edit from 1:40,000 - 800,000 softcopy stereo models vs. the traditional DEM collection from 1:40,000 analytical stereo model.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Greg A. Hermanson, John H. Hinchman, Urho A. Rauhala, and Walter J. Mueller "Comparison of two terrain extraction algorithms: hierarchical relaxation correlation and global least squares matching", Proc. SPIE 1944, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision, (24 September 1993);


Back to Top