Linear and Nonlinear Approach Fordem Smoothening

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 bymorphological 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.