HOME
Full Articles List
Search by Author
Title: AUTOMATIC RECOGNITION OF EXOGENIC LANDFORM TYPES ON THE ARCTIC TERRAIN USING SPECTRAL GEOMORPHOMETRIC VARIABLES (EXAMPLE OF THE EUROPEAN PART OF THE RUSSIA)
BibTex 
Open this article in the author's DB |
|
AUTOMATIC RECOGNITION OF EXOGENIC LANDFORM TYPES ON THE ARCTIC TERRAIN USING SPECTRAL GEOMORPHOMETRIC VARIABLES (EXAMPLE OF THE EUROPEAN PART OF THE RUSSIA)
|
|
|
|
|
|
|
|
||
|
|
|
|
|
|
1314-2704
|
|
|
|
||
|
|
English
|
|
|
|
19
|
|
|
|
2.1
|
|
|
|
|
|
|
|
||
|
|
Possibility of using of the spectral geomorphometric variables for automatic landform types classification was estimated. The area of our interest - European part of the Russian sector of Arctic region. There are four main types of landforms with exogenic origin on the territory: 1) glacial and 2) glaciofluvial accumulative terrains, 3) ridges and hills terrain from glacial exaration, 4) wave terrain from glacial exaration. These landforms types cover from 55% to 80% in the different parts of the area of interest. A few of spectral geomorphometric variables were computed by sampled 2D discrete Fourier transform of DEM by 10*10 km moving window. We use follow spectral variables for landform type recognition: 1) magnitude of the main wave in the heigth field, 2) wavelength of the main wave, 3) importance (share of the heigth variation) of the fix pool (often 1%) of biggest harmonic waves, 4-5) general direction (and its significance) of the height oscillations, 6-7) coefficients of the exponential trend equation for approximation wave magnitudes distribution. We compare five techniques for classification: 1-2) linear (LDA) and quadratic (QDA) discriminant analysis, 3) support vector machine (SVM), 4-5) classification tree (CT) and random forest (RF). It has been get that the efficiencies of the techniques (on the initial training data) are sorted respectively: 0.99 (RF), 0.62(SVM), 0.58 (CT), 0.5 (LDA), 0.45 (QDA) . This values are given versus random guessing (around 0.25). That mean the RF (for example) explained 99% of multidimensional variance. For the control data (new 20 samples of each landform types) resulting accuracies are comparable.
|
|
|
|
conference
|
|
|
|
||
|
|
||
|
|
19th International Multidisciplinary Scientific GeoConference SGEM 2019
|
|
|
|
19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
|
|
|
|
Proceedings Paper
|
|
|
|
STEF92 Technology
|
|
|
|
International Multidisciplinary Scientific GeoConference-SGEM
|
|
|
|
Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
|
|
|
|
785-792
|
|
|
|
30 June - 6 July, 2019
|
|
|
|
website
|
|
|
|
cdrom
|
|
|
|
5424
|
|
|
|
spectral analysis; landform types; supervised classification; geomorphological mapping
|
|
Download PDF