Discrete Dynamics in Nature and Society
Volume 2006 (2006), Article ID 63245, 10 pages

Linear and nonlinear approach for DEM smoothening

S. Dinesh1 and P. Radhakrishnan2

1Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia
2Faculty of Information Science and Technology (FIST), Multimedia University, Melaka 75450, Malaysia

Received 26 November 2004; Accepted 10 May 2005

Copyright © 2006 S. Dinesh and P. Radhakrishnan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


One of the biggest problems faced while analyzing digital elevation models (DEMs), particularly DEMs that are produced using photogrammetry, is to avoid pits and peaks in DEMs. Peaks and pits, which are errors, are generated during the surface generation process. DEM smoothening is an important preprocessing step meant for removing these errors. This paper discusses two linear DEM smoothening methods, Gaussian blurring and mean smoothening, and two nonlinear DEM smoothening methods, morphological smoothening and morphological smoothening by reconstruction. The four methods are implemented on a photogrammetrically generated DEM. The drainage network of the resultant DEM is obtained using skeletonization by morphological thinning, and the fractal dimension of the extracted network is computed using the box dimension method. The fractal dimensions are then compared to study the effects of the four smoothening methods. The advantages of nonlinear DEM smoothening over linear DEM smoothening are discussed. This study is useful in landscape descriptions.