Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2020 | Vol. 13, no. 1 | 103--114
Tytuł artykułu

Geospatial data processing characteristics for environmental monitoring tasks

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper explores the specifics of working with geospatial data when making decisions about the current environmental status of objects based on Earth space monitoring data. The expediency of sharing statistical data, Earth remote sensing data, and contact measurements is displayed. An analysis of the specifics of this approach to solving the problems of complex processing of multi-temporal a priori data obtained by various shooting equipment was carried out. The existing methods for combining such data are analyzed and possible options for reducing temporary resources and reducing requirements for information resources when working with large volumes of information are considered. It is appropriate to use the method of hierarchical partitioning of multi-temporal image data or images of the analyzed areas obtained at the same time, but from different satellites taking into account the specifics of the shooting equipment and subject to their correspondence to the given a priori geospatial information. One of the criteria for hierarchical partitioning is the identification of areas of greatest correspondence with a priori data with their geographical reference in satellite imagery to reduce the localization time of the corresponding zones throughout the analyzed image array. The economic application effect of this method is substantiated by reducing the computational complexity of costly pattern matching processes, as well as performance improvement of change determination algorithms in topological and geometric characteristics of these objects. An algorithm is shown for detecting changes in heterogeneity in images based on the result of overlay operations with time-differentiated satellite imagery. To confirm the adequacy of the proposed method, the results of its practical implementation are shown on the Ukraine-Poland border area. A comparative analysis of the obtained results with real data is carried out.
Wydawca

Rocznik
Strony
103--114
Opis fizyczny
Bibliogr. 24 poz.
Twórcy
  • PhD Prof.; National Aerospace University - “Kharkiv Aviation Institute”, Department of Geoinformation Technologies and Space Monitoring of Earth, Faculty of rocket and space engineering, 17, Chkalova str., Kharkiv, Ukraine, 61070
  • PhD; National Aerospace University - “Kharkiv Aviation Institute”, Department of Geoinformation Technologies and Space Monitoring of Earth, Faculty of rocket and space engineering, 17, Chkalova str., Kharkiv, Ukraine, 61070, s.horelik@khai.edu
autor
  • PhD; Director; Western Scientific Centre of the National Academy of Sciences of Ukraine and of Ministry of Education and Science of Ukraine, 4, Matejka str., Lviv, 79007
Bibliografia
  • [1] Iwaniak A. Kubik T., Tymków P. (2006). Feature extraction in high-resolution raster images using neural networks. Reports on Geodesy. Warszawa, Poland, 77(5), 263-271.
  • [2] Lyalko V.I., PopovM.A., Podorvan V.N., Sahackij A.I. (2005) Metodika klassifikacii ploshadnyh obektov na mnogospektralnyh kosmicheskih izobrazheniyah na osnove posledovatelnogo sliyaniya informacii [Classification Technique Of Square Objects On Multispectral Space Images Based On A Serial Information Merger]. Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa - Current problems in remote sensing of the Earth from space, 1(2), 89-95 [in Russian].
  • [3] Kozoderov V. V. Egorov V. D. (2011). Raspoznavanie rastitelnosti po dannym giperspektralnogo aerozondirovaniya [Vegetation recognition according to the hyperspectral aerosensing data]. Issledovanie Zemli iz kosmosa - Earth from Space, 3, 40-48 [in Russian].
  • [4] Gurevich I.B., Zhuravlev Yu.I., Smetanin Yu.G. (1999) Postroenie algebr izobrazhenij na osnove deskriptivnogo podhoda [Building an algebra of images based on a descriptive approach]. Proceeding from Reports of the 9th Russian Conference “Mathematical Methods of Pattern Recognition”, 33-36 [in Russian].
  • [5] Tarshin V.A., Sotnikov A.M., Pashenko R.E. (2014). Metod operativnoj podgotovki etalonov na osnove fraktalnoj obrabotki izobrazhenij s vysokoj obektovoj nasyshennostyu [The image set preparation for training vision system] Tehnicheskoe zrenie - Technical vision, 1(5), 2-8 [in Russian].
  • [6] Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. http://dx.doi.org/10.1080/01431160600746456.
  • [7] WorldView-4 Satellite Sensor. (n.d.). Retrieved December 02, 2019 from https://www.satimagingcorp.com/satellite-sensors/ geoeye-2/.
  • [8] Malcev G.N., Kozinov I.A. (2016). Peredacha giperspektralnyh videodannyh distancionnogo zondirovaniya zemli po radiokanalam s ogranichennoj propusknoj sposobnostyu [Transfer of Hyperspectral Video Data of Earth Remote Sensing via Radio Channels of Limited Capacity]. Informacionnye kanaly i sredy - Information and Control Systems, 2, 74-83. [in Russian].
  • [9] Labutina I. A. (2004) Deshifrirovanie aerokosmicheskih snimkov [Interpretation of aerospace images]. Moscow: Aspect Press [in Russian].
  • [10] Mokadem, D., Amine, A., Elberrichi, Z., & Helbert, D. (2018). Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images. International Journal of Organizational and Collective Intelligence, 8(1), 46-62. doi: 10.4018/ijoci.2018010104.
  • [11] Plaza, A., Martinez, P., Gualtieri, J. A., & Perez, R. (2002). . Image and Signal Processing for Remote Sensing VII. doi: 10.1117/12.454162.
  • [12] Karlov D.V., Berezina S.I., Ryeznikov Yu.V., Korobeckij O.V. (2019). Vikoristannya polya fraktalnoyi rozmirnosti dlya vektorizaciyi ob’yektiv rozvidki [Age of the fractal dimension field for vectorization of intelligence objects]. Sistemi ozbroyennya i vijskova tehnika - Systems of Arms and Military Equipment, 3(59), 64-71. DOI: 10.30748/soivt.2019.59.08.
  • [13] Butenko, O.S., Berezina, S.I., Krasovskij, G.Ya. (2008). Kompleksnyj podhod k deshifrirovaniyu snimkov po dannym kosmicheskogo monitoringa [An integrated approach to decrypting images based on space monitoring data]. Ekologiya j resursi: zbirnik naukovih prac Institutu problem nacionalnoyi bezpeki - Ecology and resources: a collection of scientific papers of the Institute for National Security, 1, 23-41.
  • [14] Henrique dos Santos, P., Neves, S. M., Sant’Anna, D. O., Henrique de Oliveira, C., Carvalho, H. D. (2018). The analytic hierarchy process supporting decision making for sustainable development: an overview of applications. Journal of Cleaner Production, 212, 119-138. doi:10.1016/j.jclepro.2018.11.270.
  • [15] Butenko, O.S. (2010). Scenarij alternative razvitiya izmeneniya sostoyaniya anomalnyh ekologicheskih obektov pri kompleksnom vozdejstvii vozmushenij [The scenario of alternatives to the development of the state change of anomalous ecological objects under the complex influence of disturbances]. Otkrytye informacionnyei kompyuternye integrirovannye tehnologii - Open information and computer integrated technologies, 46, 225-237.
  • [16] Cheng,G, Han, J. (2016 ). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28.
  • [17] Masuda, T. (1990). Hierarchical sensitivity analysis of priority used in analytic hierarchy process. International Journal of Systems Science, 21(2), 415-427. doi:10.1080/00207729008910371.
  • [18] Kachinskij, A. B. (2003). Bezpeka, zagrozi i rizik: naukov i koncepciyi ta matematichni metodi (Threats and risk: scientific concepts and mathematical methods). Kiev: Institute of problems National security [in Russian].
  • [19] Basarab, M. A., Volosyuk, V. K., Goryachkin, O. V., Zelenskij, A. A., Kravchenko, V. F., Ksendzuk, A. V. et al. (2007). Cifrovaya obrabotka signalovi izobrazhenij v radiofizicheskih prilozheniya
  • [Digital signal and image processing in radiophysical applications]. Moscow: FIZMATLIT.
  • [20] Volosyuk, V.K., Kravchenko, V.F. (2008). Statisticheskaya teoriya radiotehnicheskih sistem distancionnogo zondirovaniya i radiolokacii [Statistical theory of radio systems for remote sensing and radar]. Moscow: FIZMATLIT.
  • [21] Saaty, T. L., & Vargas, L. G. (2012). The seven pillars of the analytic hierarchy process, models, methods, concepts & applications of the analytic hierarchy process. International Series in Operations Research & Management Science, 175, 23-40, Springer 978-1- 4614-3596-9.
  • [22] Jalaliyoon, N., Bakar, N. A., Taherdoost, H. (2012). Accomplishment of Critical Success Factor in Organization; Using Analytic Hierarchy Process. International Journal of Academic Research in Management, Helvetic Editions Ltd, 1(1), 1-9.
  • [23] Sentinel-hub EO-Browser. (n.d.). Retrieved from November 13, 2019, https://apps.sentinel-hub.com/eobrowser/
  • [24] CSI SRTM - SRTM 90m DEM Digital Elevation Database. (n.d.). Retrieved November 22, 2019, from http://srtm.csi.cgiar.org/.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-d863a962-46a7-43e6-9806-fac6ea15a8b2
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.