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USING OF THE SPECTRAL GEOMORPHOMETRIC CHARACTERISTICS FOR AUTOMATIZED CLASSIFICATION OF LANDFORMS (ON THE EXAMPLE OF AUSTRALIA)

Sergey Kharchenko

First published: 2018-06-20https://doi.org/10.5593/sgem2018/2.3/s11.091View metrics

Abstract

The algorithm for computation of the spectral characteristics of terrain was prepared. The periodicity of Earth surface is specific В«fingerprintВ» of the cooperation of geological structure, exogenic processes and history of nature development for geomorphologically detached areas. For computing we used DEMs and 2D discrete Fourier transform with moving quadratic window of different sizes. Matrix of elevations decomposes in the Fourier image which defines harmonic waves set. All set of waves determine height field with absolute accuracy. However fixed part of waves allows reconstructing DEM with the different accuracy according to specific topographical situation. We can use this effect for landform classification. The follow characteristics was calculated: maximum of the waves magnitudes, the importance of given share of waves, the general direction of heights fluctuation, the severity of this direction, frequencies of the biggest wave by the orthogonal directions (W-E and S-N) and the general wavelength. The parameters of frequencies depends of window size (they are expressed in units/parts of window: 1/1, 1/2, 1/3 of window size and others). So we use only five parameters without frequencies for the classification. The used method of the landforms delineation on the Australia territory is Kohonen selforganizing maps (neural network). On the quadratic template net (15*15 neurons) we projected five-dimensional data and visualize the results as map of Sammon. On the finish we created the map of landforms types in Australia in the context of terrain periodicity.

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Publication details

Title
USING OF THE SPECTRAL GEOMORPHOMETRIC CHARACTERISTICS FOR AUTOMATIZED CLASSIFICATION OF LANDFORMS (ON THE EXAMPLE OF AUSTRALIA)
Authors
Sergey Kharchenko
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2018
Pages
719-724
SWS Citekey
Kharchenko201811719724
ISSN
1314-2704
ISBN
978-619-7408-41-6
Language
en
Publication type
Conference Paper
Keywords
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Number of times cited according to Crossref: 1

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