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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 6, pp. 129-143

Detection of perennial heaving mounds in digital elevation model images using decomposition by topological features

S.V. Eremeev 1 , A.V. Abakumov 1 , D.E. Andrianov 1 , I.V. Bogoyavlensky 2 , R.A. Nikonov 2 
1 Murom Institute (Branch) of Vladimir State University, Murom, Russia
2 Oil and Gas Research Institute RAS, Moscow, Russia
Accepted: 27.11.2023
DOI: 10.21046/2070-7401-2023-20-6-129-143
In the past decade, interest in studying the geocryological features of the Arctic has increased significantly, which is associated with the expansion of the search for and development of mineral resources, especially hydrocarbons. The article describes the technology for automated detection of perennial heaving mounds (PHM), often posing the threat of powerful gas blowouts and explosions, on digital elevation models (DEMs). ArcticDEM high-precision DEMs of the Arctic, built by photogrammetric processing of high-resolution satellite images, are used as initial data. The developed PHM search technology, including software, is based on the use of image decomposition based on topological features. This method allows one to generate a set of topological properties from the input matrix of DEM points and carry out their further analysis with subsequent filtering according to specified parameters. The features of PHM presentation are shown, as well as their differences from other relief-forming objects of the Arctic. Numerical and graphical results of PHM detection using real data from the north of Western Siberia are presented. The proposed approach makes it possible to analyze the DEM by geometric and topological features. With the use of geometric features, the average accuracy of correctly selected objects is 78.2 %, and with the addition of topological properties, this value increases to 81.8 %.
Keywords: perennial heaving mounds (PHM), digital elevation models (DEM), ArcticDEM, image decomposition, topological features
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