Improved model for formation of views in geospatial information processing systems

DOI: 10.31673/2412-4338.2020.043017

Abstract

The subject of research in the work is a model of the formation of species images in systems for processing geospatial information. The purpose of the article is to improve the model of formation of species images in systems for processing geospatial information, which will take into account the shortcomings of existing models of formation of species images. The advantages of obtaining and application of geospatial information are given. It has been established that in systems for processing geospatial information for various purposes at different stages, data of various types are processed, however, they are structurally isomorphic - geospatial information structures. To solve the problem of representing heterogeneous software engineering technologies in a unified form, convenient for their integration and coordination within the general cycle of software systems design, the mathematical apparatus of category theory has recently been used. The existing models of the formation of species images in systems for processing geospatial information, their disadvantages and advantages are considered. The considered mathematical model of the formation of a view image in general form can be presented as a result of the action of an operator performing the transformation of coordinates and operators determining the brightness of the corresponding image element for a given element of the earth's surface in the spectral channel. An improved model of the formation of species images is proposed, which simultaneously takes into account the transformation of geospatial coordinates into image coordinates and the transformation of brightness due to the properties of objects on the earth's surface and the processes of the passage of solar radiation in the atmosphere, provides the possibility of correct processing and analysis of species images in geospatial information processing systems.

Keywords: management decision making, geospatial information, species imaging, imaging model, geospatial coordinates.

References
1. Shikhov A. N., Cherepanova E. S. and Ponomarchuk A. I. (2014), “Geoinformation systems: the use of GIS technologies in solving hydrological problems”: a workshop, textbook. Manual. Perm, 91 p.
2. "Earth Observing System. High-Resolution Satellite Imagery" (2020), https://eos.com/ru/products/high-resolution-images/. 05 March 2020.
3. Filipovych V. Ye. (2015), "Operational control over the spread of illegal amber mining and assessment of damage caused to the state, based on multi-zone space survey". Ecological safety and nature management, № 4(20). P. 92–97.
4. “Management decisions: heuristics, creativity, transparency”: a textbook (2008), Pid red. M.P.Butka. Nizhy`n: TOV «Vy`davny`cztvo «Aspekt-Poligraf». 428 p.
5. Savchenko A. S. and Sy`nel`nikov O. O. (2017), “Methods and systems of artificial intelligence.” Ky`yiv: NAU. 190 p.
6. Kushly`k-Dy`vul`s`ka O. I. and Kushly`k B. R. (2014), “Fundamentals of decision theory.” Ky`yiv, 94 p.
7. Andronov V. H. Lazareva E. V. (2011), Coordinate referencing and orthocorrection of space images in panoramic shooting modes with scanning with a head mirror. System analysis, management and information processing. Р. 19–24.
8. Andronov V. H. Dehtiarjov S.V. Lazareva E. V. (2012), Photogrammetric model of space panoramic images. News of universities. Instrumentation. Vol. 55. № 1. Р. 19–24.
9. Kadochnykov A. P. Kazantsev A. A. Myshukov O. A. Shyhorev S.A. (2019), Formation of models of radar images in the form of stochastic differential equations for recognition of space objects. Proceedings of educational institutions. Vol. 5. № 4. P. 106–113.
10. Kovalёv S. P. (2014), "Category-theoretic approach to the design of software systems". Fundamental and Applied Mathematics. 19(3). P.111–170.
11. Abdel-Aziz Y. I. and Karara H. M. (1971), "Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates". ASP Symposium on Close-Range Photogrammtery, Falls Church, VA. Р. 1–18.
12. Zhang J. and Zhang X. (2003), "Strict geometric model based on affine transformation for remote sensing image with high resolution". International Archives of Photogrammetry and Remote Sensing. № 34 (B3). P 309–312.
13. Morgan M., Kim K., Jeong S. and Habib A. (2004), "Parallel projection modelling for linear array scanner scenes". XXth Congress of ISPRS, 12-23 July, 2004.
14. Gnatushenko V. V. (2004), "Alternative geometric models for obtaining high-resolution satellite images". Geometric and computer modeling. 8. P. 48–53.
15. Gnatushenko V. V. (2004), "Geometric methods of forming high-resolution images from the IKONOS satellite". Proceedings of the Tavriya State Agrotechnical Academy. 4. (27). P. 35–39.
16. Forsyth David, Ponce Jean (2004), “Computer vision. A Modern Approach.” Moskva: Vy`l`yams. 928 p.
17. Hartley R. and Zisserman A. (2003), “Multiple View Geometry in Computer Vision (2nd ed.).” UK: Cambridge University Press. 670 p.
18. Zhang Z. (1998), A Flexible New Technique for Camera Calibration. MSR-TR-98-71. Microsoft Research. [Electronic resource]. URL: https://www.researchgate.net/publication/3193178_A_Flexible_New_Technique_for_Camera_Calibration. 19 Jan. 2020.
19. Peter A. Burrough and Rachael A. McDonnell (1998), “Principles of Geographical Information Systems”. Oxford, Oxford University Press. 333 p.
20. Schott J. (2007), “Remote Sensing: The Image Chain Approach (2nd ed.).” Oxford, Oxford University Press 668 p.
21. Robert J. Woodham (1980), "Using Digital Terrain Data To Model Image Formation In Remote Sensing," Proc. SPIE 0238, Image Processing for Missile Guidance, doi: 10.1117/12.959167.
22. Makoveichuk O. M. and Podlipayev V. O. (2005), "Algorithms for restoration of defocused images". Weapons systems and military equipment. № 3-4. P. 99–103.
23. Mashkov O.A., Sobchuk V.V., Barabash O.V., Dakhno N.B., Shevchenko H.V. and Maisak T.V. (2019), “Improvement of variational-gradient method in dynamical systems of automated control for integro-differential models”. Mathematical Modeling and Computing. Vol. 6. No. 2. P. 344 – 357.
24. Barabash O.V., Dakhno N.B., Shevchenko H.V. and Majsak T.V. (2017), “Dynamic Models of Decision Support Systems for Controlling UAV by Two-Step Variational-Gradient Method”. Proceedings of 2017 IEEE 4th International Conference “Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD)”, October 17-19, 2017, Kyiv, Ukraine. National Aviation University. P. 108 – 111.
25. Barabash O.V., Bodrov S.V. and Musiienko A.P. (2014), “Analysis of the construction of a network of video surveillance of customs observation posts based on a functionally stable system”. Scientific and practical journal "Communication". Issue 2. P. 8 – 11.
26. Makovejchuk A. N., Podly`paev V. A. and Xudov G. V. (2005), "Theoretical substantiation of the method of protection of species images from the effects of masking interference". Information processing systems. 6. P. 62–71.
27. Butko I. M. and Makoveichuk O. M. (2012), "Identification of forest fire sources based on satellite images". Scientific Bulletin of NLTU of Ukraine. 22.3. P. 80-84. Access mode: http://nbuv.gov.ua/UJRN/nvnltu_2012_22.