Weitere Kapitel dieses Buchs durch Wischen aufrufen
The classification of terrain by its passability plays a significant role in the process of Intelligence Preparation of the Battlefield (IPB). In the process of developing passability maps, the classification of terrain to a specific class (GO, SLOW-GO, NO-GO). In this paper the problem of terrain classification to the respective category of passability was solved by the application of Self Organizing Maps by generating a continuous Index of Passability (IOP), which characterizes the terrain in a range from 0 (the impassable area) to 1 (the area of high manoeuvrability). The article describes the methodology of using this type of network to develop a terrain passability map. As a “case of use”, three voivodeships located in the north-eastern part of Poland were selected. To prepare a training set, topographic vector data from VMap L2 and SRTM (Shuttle Radar Topography Mission) digital terrain model were used. Research was conducted on a primary grid field with dimensions 1 km × 1 km. As a result of the research conducted, normalised parameters associated with terrain cover were introduce into the neural network. As a result of the network learning, the analysed area was divided into classes, to which the index of passability (IOP) was arbitrarily subordinated. In the research results, the influence of the method of organisation of the input data on the generated maps of passability was defined. The tests were conducted on two sizes of a Kohonen map: 10 × 10 and 5 × 5 neurons. The described experiments proved that a properly taught artificial neural network is very well suited to the analysis of an area in terms of passability. The presented methodology is universal in nature and after the modification of parameters may be used to solve tasks of terrain classification associated with various subjects (division of soils, marking out areas for development, etc.).
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Bagheri Bodaghabadi, M., Martinez-Casasnovas, J. A., Salehi, M. H., Mohammadi, J., Esfandiarpoor Borujeni, I., et al. (2015). Digital soil mapping using artificial neural networks and terrain-related attributes. Pedosphere, 25(4), 580–591. doi: 10.1016/S1002-0160(15)30038-2
Bielecka, E., Pokonieczny, K., & Kamiński, P. (2014). Study on spatial distribution of horizontal geodetic control points in rural areas. Acta Geodaetica et Geophysica, 49(3), 357–368. doi: 10.1007/s40328-014-0056-6
Campbell, L., Lotwin, A., DeRico, M. M. G., & Ray, C. (1997). The use of artificial intelligence in military simulations. In SMC ‘97 Conference Proceedings—1997 IEEE International Conference on Systems, Man, and Cybernetics, Vols. 1–5: Conference Theme: Computational Cybernetics and Simulation, Book Series: IEEE International Conference on Systems, Man, and Cybernatics (pp: 2607–2612).
Dodge, Y. (Ed.). (2003). A dictionary of statistics. Oxford: Oxford University Press. ISBN 0-19-850994-4.
Field manual 5-33 Terrain Analysis. (1990). Headquarters, Department of US Army.
Glinton, R., Giampapa, J., Owens, S., Sycara, K., Grindle, C., & Lewis, M. (2004). Integrating context for information fusion: Automating intelligence preparation of the battlefield, Human performance, situation awareness and automation: Current research and trends. In 2nd Conference on Human Performance, Situation Awareness and Automation (HPSAA II), Daytona Beach, FL (Vol. 2, pp: 224–229), March 22–25, 2004.
Grebby, S., Naden, J., Cunningham, D., & Tansey, K. (2011). Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain. Remote Sensing Of Environment, 115(1), 214–226. doi: 10.1016/j.rse.2010.08.019
Han, H., Chung, W., Song, J., Seol, A., & Chung, J. (2016). A terrain-based method for selecting potential mountain ridge protection areas in South Korea, Landscape Research, 41(8), 906–921. doi: 10.1080/01426397.2016.1173657
Hofmann, A., Hoskova-Mayerova, S., Talhofer, V., & Kovarik, V. (2014). Creation of models for calculation of coefficients of terrain passability. Quality & Quantity, 49(4), 1679–1691. doi: 10.1007/s11135-014-0072-1
Irvin, B. J., Ventura, S. J., & Slater, B. K. (1997). Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin. Geoderma, 77(2–4), 137–154. doi: 10.1016/S0016-7061(97)00019-0
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69. doi: 10.1007/BF00337288
Kohonen, T., Oja, E., Simula, O., Visa, A., & Kangas, J. (1996). Engineering applications of the self-organizing map. Proceedings of the IEEE, 84(10), 1358–1384. doi: 10.1109/5.537105
Lee, E. S., & Kim, J. B. (2014). A utilization study of domestic thematic map for military terrain analysis cartography. In 2014 International Conference on Information Science and Applications (ICISA): Book Series: International Conference on Information Science and Applications. doi: 10.1109/ICISA.2014.6847347
Lee, S., Song, K. Y., Kim, Y., & Park, I. (2012). Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model, Hydrogeology Journal, 20(8), 1511–1527. doi: 10.1007/s10040-012-0894-7
Medyńska-Gulij, B. (2010). Map compiling, map reading, and cartographic design in Pragmatic pyramid of thematic mapping. Quaestiones Geographicae, 29(1), 57–63. doi: 10.2478/v10117-010-0006-5.
Miller, D., Rueter, S., Miller, D. F., & Rueter, S. P. (2013) Mission adaptable terrain analysis system used by military planner, has transform components which transform data having basic terrain evaluations to input data format. Patent Number(s): EP2554944-A2; US2013035861-A1; EP2554944-A3.
NATO Geospatial Policy (MC 296/1).
NO-06-A015:2012, Terrain—Rules of classification—Terrain analysis on operational level (2012).
Pokonieczny, K., Bielecka, E., & Kaminski, P. (2014). Analysis of spatial distribution of geodetic control points and land cover. Geoconference on Informatics, Geoinformatics and Remote Sensing, Vol II, Book Series: International Multidisciplinary Scientific GeoConference-SGEM (pp. 49–56). Published: 2014 Conference: 14th International Multidisciplinary Scientific Geoconference (SGEM), Albena, BULGARIA, June 17–26, 2014.
Richbourg, R., & Olson, W. K. (1996). A hybrid expert system that combines technologies to address the problem of military terrain analysis. Expert Systems Applications, 11(2), 207–225. doi: 10.1016/0957-4174(96)00033-4
Shuttle Radar Topography Mission, NASA, http://www2.jpl.nasa.gov/srtm, Access: July 1, 2016.
STANAG 2259, Ed. 4: Military Geographic Documentation—Terrain. NATO standardization agreement.
STANAG 3992, Ed. 2: Military Geographic Documentation—Terrain Analysis AgeoP-1 (A). NATO standardization agreement.
STANAG 7074, Ed. 2: Digital Geographic Information Exchange Standard (DIGEST). NATO standardization agreement.
StatSoft, https://www.statsoft.com/, Access: Novenber 15, 2016.
Suzuki, K. (2013). Artificial neural networks—Architectures and applications (p. 264). InTech. ISBN 978-953-51-0935-8.
- Methods of Using Self-organising Maps for Terrain Classification, Using an Example of Developing a Military Passability Map
Neuer Inhalt/© ITandMEDIA, Best Practices für die Mitarbeiter-Partizipation in der Produktentwicklung/© astrosystem | stock.adobe.com