NDLI: A framework for generating high quality digital elevation models in urban areas

Content Provider	IEEE Xplore Digital Library
Author	Elaksher, A.F.
Copyright Year	2008
Description	Author affiliation: Fac. of Eng., Cairo Univ., Beni-Suef (Elaksher, A.F.)
Abstract	Digital elevation models (DEMs) are very important for a variety of applications, such cartographic analysis, mobile communication, urban planning, and visualization. A conventional stereo pair is not sufficient to generate a complete and accurate digital elevation model (DEM) particularly in urban areas. Hence, a robust technique for DEMs generation is vital. This paper presents a technique that can match a large number of overlapping images. The traditional cross correlation technique is used to generate a good approximation of the DEM. Least squares matching is then used to generate a higher accuracy DEM. Due to discontinuities that result from man made features, the generated DEM still lack the complete surface representation. A refinement process is then applied to the generated DEM. The refinement process utilizes the geometrical properties of man made features such as buildings. Points contributing to buildings are delineated and used to fit planes in the object space. A robust estimation technique is used in this step to guarantee elimination of outliers. The final results after this refinement step looks appealing and suggest the use of this algorithm for surface reconstruction.
Starting Page	169
Ending Page	172
File Size	838720
Page Count	4
File Format	PDF
ISBN	9781424422968
DOI	10.1109/SSIAI.2008.4512312
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Digital elevation models Urban areas Robustness Surface fitting Mobile communication Urban planning Visualization Least squares approximation Least squares methods Surface reconstruction Image Segmentation Correlation Least Squares matching Robust Estimation
Content Type	Text
Resource Type	Article

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