The Effect of Gaussian Blurring on the Extraction of Peaks and Pits from Digital Elevation Models | Zendy

A. Pathmanabhan | Zendy; S. Dinesh | Zendy

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The Effect of Gaussian Blurring on the Extraction of Peaks and Pits from Digital Elevation Models

Author(s) -

A. Pathmanabhan,

S. Dinesh

Publication year - 2007

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2007/62137

Subject(s) - gaussian , gaussian blur , digital elevation model , gaussian filter , gaussian network model , smoothing , spurious relationship , gaussian noise , computer science , elevation (ballistics) , artificial intelligence , gaussian function , computer vision , mathematics , geology , remote sensing , physics , image processing , image (mathematics) , geometry , image restoration , quantum mechanics , machine learning

Gaussian blurring is an isotropic smoothing operator that is used to remove noise and detail from images. In this paper, the effect of Gaussian blurring on the extraction of peaks and pits from digital elevation models (DEMs) is studied. First, a mathematicalmorphological-based algorithm to extract peaks and pits from DEMs is developed.Gaussian blurring is then implemented on the global digital elevation model(GTOPO30) of Great Basin using Gaussian kernels of different sizes and standarddeviation values. The number of peaks and pits extracted from the resultant DEMs iscomputed using connected component labeling and the results are compared. Theapplication of Gaussian blurring to perform the treatment of spurious peaks and pits inDEMs is also discussed. This work is aimed at studying the capabilities of Gaussianblurring in the modeling of objects and processes operating within an environment

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