Abstract
A key problem of any video volcano surveillance network is an inconsistent quality and information value of the images obtained. To timely analyze the incoming data, they should be pre-filtered. Additionally, due to the continuous network operation and low shooting intervals, an operative visual analysis of the shots stream is quite difficult and requires the application of various computer algorithms. The article considers the parametric algorithms of image analysis developed by the authors for processing the shots of the volcanoes of Kamchatka. They allow automatically filtering the image flow generated by the surveillance network, highlighting those significant shots that will be further analyzed by volcanologists. A retrospective processing of the full image archive with the methods suggested helps to get a data set, labeled with different classes, for future neural network training.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Girina, O.A., Gordeev, E.I.: KVERT project: reduction of volcanic hazards for aviation from explosive eruptions of Kamchatka and Northern Kuriles volcanoes. Vestnik FEB RAS 132(2), 100–109 (2007)
Gordeev, E.I., Girina, O.A., Loupian E.A., Sorokin, A.A., Kramareva, L.S., Efremov, V.Yu., Kashnitskii, A.V., Uvarov, I.A., Burtsev, M.A., Romanova, I.M., Melnikov, D.V., Manevich, A.G., Korolev, S.P., Verkhoturov, A.L.: The VolSatView information system for monitoring the volcanic activity in Kamchatka and on the Kuril Islands. J. Volcanol. Seismolog. 10(6), 382–394 (2016). https://doi.org/10.1134/S074204631606004X
Sorokin, A.A., Korolev, S.P., Malkovsky, S.I.: The signal automated information system: research and operational monitoring of dangerous natural phenomena in the Russian Far East. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 16(3), 238–248 (2019). https://doi.org/10.21046/2070-7401-2019-16-3-238-248
Malkovsky, S.I., Sorokin, A.A., Girina, O.A.: Development of an information system for numerical modelling of the propagation of volcanic ash from Kamchatka and Kuril volcanoes. Comput. Technol. 24(6), 79–89 (2019). https://doi.org/10.25743/ICT.2019.24.6.010
Ando, B., Pecora, E.: An advanced video-based system for monitoring active volcanoes. Comput. Geosci. 32(1), 85–91 (2006). https://doi.org/10.1016/j.cageo.2005.05.004
Viteri, F., Barrera, K., Cruz, C., Mendoza, D.: Using computer vision techniques to generate embedded systems for monitoring volcanoes in Ecuador with trajectory determination. J. Eng. Appl. Sci. 13(3), 3164–3168 (2018). https://doi.org/10.3923/jeasci.2018.3164.3168
Rabal, H.J., Braga, J.R.A.: Dynamic Laser Speckle and Applications. CRC Press, Boca Raton (2009)
Kramareva, L.S., Andreev, A.I., Bloshchinskiy, V.D., Kuchma, M.O., Davidenko, A.N., Pustatintsev, I.N., Shamilova, Y.A., Kholodov, E.I., Korolev, S.P.: The use of neural networks in hydrometeorology problems. Comput. Technol. 24(6), 50–59 (2019). https://doi.org/10.25743/ICT.2019.24.6.007
Sorokin, A., Korolev, S., Romanova, I., Girina, O., Urmanov, I.: The Kamchatka volcano video monitoring system. In: Proceedings of 2016 6th International Workshop on Computer Science and Engineering (WCSE 2016), pp. 734–737. The Science and Engineering Institute, LA, CA, USA (2016)
Kamaev, A.N., Urmanov, I.P., Sorokin, A.A., Karmanov, D.A., Korolev, S.P.: Images analysis for automatic volcano visibility estimation. Comput. Opt. 42(1), 128–140 (2018). https://doi.org/10.18287/2412-6179-2018-42-1-128-140. (in Russian)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Urmanov, I., Kamaev, A., Sorokin, A.: Computer methods of image processing of volcanoes. In: Proceedings of the IV International Research Conference “Information Technologies in Science, Management, Social Sphere and Medicine” (ITSMSSM 2017), vol. 72, pp. 371–374. Atlantis Press, Paris (2017). https://doi.org/10.2991/itsmssm-17.2017.77
Borgefors, G.: Distance transformations in digital images. CVGIP 34(3), 344–371 (1986). https://doi.org/10.1016/S0734-189X(86)80047-0
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94
Shmilovici, A.: Support vector machines. In: Data Mining and Knowledge Discovery Handbook. Springer, Boston (2009). https://doi.org/10.1007/0-387-25465-X_12
Schölkopf, B., Tsuda, K., Vert, J.P.: Kernel Methods in Computational Biology. MIT Press, Cambridge (2004)
Kamaev, A.N., Korolev, S.P., Sorokin, A.A., Urmanov, I.P.: Detection of thermal anomalies in the images of volcanoes taken at night. J. Comput. Syst. Sci. Int. 59(1), 95–104 (2019). https://doi.org/10.1134/S106423071906008X
Acknowledgements
The reported study was funded by RFBR, project number 20-37-70008. Computations were performed with the methods and techniques developed under the RFBR, project number 18-29-03196.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Korolev, S., Urmanov, I., Kamaev, A., Girina, O. (2020). Parametric Methods and Algorithms of Volcano Image Processing. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1295. Springer, Cham. https://doi.org/10.1007/978-3-030-63319-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-63319-6_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63318-9
Online ISBN: 978-3-030-63319-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)