Linear and nonlinear approach for DEM 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 pre...

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Main Authors: Dinesh, S., Radhakrishnan, P.
Format: Article
Language:English
Published: 2006
Subjects:
Online Access:http://shdl.mmu.edu.my/2084/
http://shdl.mmu.edu.my/2084/1/1422.pdf
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author Dinesh, S.
Radhakrishnan, P.
author_facet Dinesh, S.
Radhakrishnan, P.
author_sort Dinesh, S.
building MMU Institutional Repository
collection Online Access
description 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.
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spelling mmu-20842011-09-23T02:39:03Z http://shdl.mmu.edu.my/2084/ Linear and nonlinear approach for DEM smoothening Dinesh, S. Radhakrishnan, P. QA Mathematics 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. 2006 Article NonPeerReviewed application/pdf en http://shdl.mmu.edu.my/2084/1/1422.pdf Dinesh, S. and Radhakrishnan, P. (2006) Linear and nonlinear approach for DEM smoothening. Discrete Dynamics in Nature and Society, 2006. p. 1. ISSN 1026-0226 http://dx.doi.org/10.1155/DDNS/2006/63245 doi:10.1155/DDNS/2006/63245 doi:10.1155/DDNS/2006/63245
spellingShingle QA Mathematics
Dinesh, S.
Radhakrishnan, P.
Linear and nonlinear approach for DEM smoothening
title Linear and nonlinear approach for DEM smoothening
title_full Linear and nonlinear approach for DEM smoothening
title_fullStr Linear and nonlinear approach for DEM smoothening
title_full_unstemmed Linear and nonlinear approach for DEM smoothening
title_short Linear and nonlinear approach for DEM smoothening
title_sort linear and nonlinear approach for dem smoothening
topic QA Mathematics
url http://shdl.mmu.edu.my/2084/
http://shdl.mmu.edu.my/2084/
http://shdl.mmu.edu.my/2084/
http://shdl.mmu.edu.my/2084/1/1422.pdf