Abstract spatial structures of point objects and their application in geoinformatics-Reference-Cited by-同舟云学术

Abstract spatial structures of point objects and their application in geoinformatics

Published:2021-12-23 Issue:4 Volume: Page:28-34
ISSN:1609-364X
Container-title:Geoinformatika
language:
Short-container-title:Geoinformatika

Author:

Eremeev Sergey¹

Affiliation:

1. Murom Institute (branch), Vladimir State University named after Alexander and Nikolay Stoletovs

Abstract

The problem of the formation of abstract spatial structures and their location is considered in the article . Two algorithms for constructing abstract data and their influence on the topological relationships between the obtained objects in the form of separate clusters are shown . The first algorithm is based on the criterion of the minimum distance between points. The second algorithm uses information about vectors obtained from points and the angles between them. The practical application of abstract structures in geoinformatics for the analysis of topology between formed objects is demonstrated.

Publisher

Federal State Budgetary Institution - All-Russian Research Geological Oil Institute

Subject

General Medicine

Reference8 articles.

1. Eremeev S.V., Andrianov D.E., Titov V.S. An algorithm for matching spatial objects of different-scale maps based on topological data analysis. Computer Optics. 2019;43(6):1021–1029. DOI: 10.18287/2412-6179-2019-43-6-1021-1029.

2. Eremeev S.V., Abakumov A.V. Software complex for detection and classification of natural objects based on topological analysis. Software & Systems. 2021;34(2):201–208. DOI: 10.15827/0236-235X.133.005-018.

3. Eremeev S.V., Andrianov D.E., Kovalev Y.A. Algorithm for the identification of time evolutions of spatially distributed objects based on Barcodes. Geoinformatika. 2018;4:23–29.

4. Pestunov I.A., Rylov S.A., Berikov V.B. Hierarchical clustering algorithms for segmentation of multispectral images. Optoelectronics, Instrumentation and Data Processing. 2015;51(4):329–338. DOI: 10.3103/S8756699015040020.

5. Zimichev E.A., Kazanskiy N.L., Serafimovich P.G. Spectral-spatial classification with k-means++ particional clustering. Computer Optics. 2014;38(2):281-286. DOI: 10.18287/0134-2452-2014-38-2-281-286.

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