Improving the rail
transport security requires development
and implementation of neoteric monitoring and control facilities in conditions
of increasing speed and intensity of the train movement and high level of terrorist threat. Use of Earth remote sensing
(ERS), permitting to obtain information from large areas with a sufficiently
high resolution, can provide significant assistance in solving the mentioned
problems. This paper discusses the possibility of using various means of remote
sensing such as satellites and unmanned aerial vehicles (UAV), also known as
drones, for receiving information in different ranges of the electromagnetic
spectrum. The paper states that joint using of both these means gives new
possibilities in improving railroad security.
References
[1]
Flammini, F., Pragliola, C. and Smarra, G. (2016) Railway Infrastructure Monitoring by Drones. International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference, Toulouse, 2-4 November.
https://doi.org/10.1109/ESARS-ITEC.2016.7841398
[2]
Eyre-Walker, R.E.A. and Earp, G.K. (2008) Application of Aerial Photography to Obtain Ideal Data for Condition Based Risk Management of Rail Networks. The 4th IET International Conference on Railway Condition Monitoring, Derby, 18-20 June 2008. https://doi.org/10.1049/ic:20080353
[3]
Francesco, F., Naddei, R., Pragliola, C. and Smarra, G. (2016) Towards Automated Drone Surveillance in Railways: State-of-the-Art and Future Directions. International Conference on Advanced Concepts for Intelligent Vision Systems, Lecce, 24-27 October 2016, 336-348. https://doi.org/10.1007/978-3-319-48680-2_30
[4]
Schwank, M. and Naderpour, R. (2018) Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects. Remote Sensing, 10, 354.
https://doi.org/10.3390/rs10020354
[5]
Skofronick-Jackson, G., Kim, M., Weinman, J.A. and Chang, D.-E. (2004) A Physical Model to Determine Snowfall Over Land by Microwave Radiometry. IEEE Transactions on Geoscience and Remote Sensing, 42, 1047-1058.
https://doi.org/10.1109/TGRS.2004.825585
[6]
Besada, J.A., Bergesio, L., Campaña, I., Vaquero-Melchor, D., López-Araquistain, J., Bernardos, A.M. and Casar, J.R. (2018) Drone Mission Definition and Implementation for Automated Infrastructure Inspection Using Airborne Sensors. Sensors, 18, 1170. https://doi.org/10.3390/s18041170
[7]
Koyama, C.N. and Sato, M. (2015) Detection and Classification of Subsurface Objects by Polarimetric Radar Imaging. IEEE Radar Conference, Johannesburg, 27-30 October 2015, 440-445. https://doi.org/10.1109/RadarConf.2015.7411924
[8]
Bulgakov, A., Evgenov, A. and Weller, C. (2015) Automation of 3D Building Model Generation Using Quadrotor. Procedia Engineering, 123, 101-109.
https://doi.org/10.1016/j.proeng.2015.10.065
[9]
Hartley, R. and Zisserman, A. (2004) Multiple View Geometry in Computer Vision. 2nd Edition, Cambridge University Press, Cambridge.
https://doi.org/10.1017/CBO9780511811685
[10]
McGlone, J.C. (2013) Manual of Photogrammetry. Sixth Edition, American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, USA, 1318 p.
[11]
Lowe, D.G. (1999) Object Recognition from Local Scale-Invariant Features. Proceedings of the International Conference on Computer Vision, Kerkyra, 20-27 September 1999, Vol. 2, 1150-1157. https://doi.org/10.1109/ICCV.1999.790410
[12]
Fischler, M. and Bolles, R. (1981) Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24, 381-395. https://doi.org/10.1145/358669.358692
[13]
Maenpaa, T. and Pietikainen, M. (2005) Texture Analysis with Local Binary Patterns. In: Handbook of Pattern Recognition and Computer Vision, 3rd Edition, World Scientific, Singapore, 197-216. https://doi.org/10.1142/9789812775320_0011