THE ESTIMATION OF VIDEO SYSTEM QUALITY DETECTION OF FOREST FIRES

Kryukova M S; Shidlovsky A L; Fahmi Sh S

doi:doi:

Home / Journals / Problems of risk management in the technosphere / Volume 2018 Issue 2 / THE ESTIMATION OF VIDEO SYSTEM QUALITY DETECTION OF FOREST FIRES

THE ESTIMATION OF VIDEO SYSTEM QUALITY DETECTION OF FOREST FIRES

Submit manuscript

To cite

Citations:

THE ESTIMATION OF VIDEO SYSTEM QUALITY DETECTION OF FOREST FIRES

Journal: PROBLEMS OF RISK MANAGEMENT IN THE TECHNOSPHERE Volume 2018 № 2 , 2018

Rubrics: RISK REDUCTION AND ELIMINATION OF CONSEQUENCES OF EMERGENCY SITUATIONS. ENSURING SAFETY IN CASE OF EMERGENCY

Kryukova M S ¹

Shidlovsky A L ²

Fahmi Sh S ³

Author and publication information

Authors:

1. Saint-Petersburg university of State fire service of EMERCOM of Russia

2. Saint-Petersburg university of State fire service of EMERCOM of Russia

3. Solomenko institute of transport problems of the Russian Academy of sciences. The first electrotechnical university (ETU)

Type:

Article

Pages:

from 63 to 73

Status:

Published

Received:

12.06.2018

Accepted:

20.06.2018

Published:

25.06.2018

Language:

Russian

Keywords:

forest fires, video system, remote sensing of the earth, monitoring, detection, image analysis, segmentation

Abstract and keywords

Abstract (English):
The paper deals with the method, algorithms and basic functions for solving problems of detection, localization and structuring of image objects. The algorithm of the analysis of space images for allocation of areas of forest fires is offered. Algorithms of image analysis based on segmentation methods are investigated. The program complex of video system for automated detection of forest fires by means of images received from satellites of remote sensing of the Earth is developed and tested.

Keywords:
forest fires, video system, remote sensing of the earth, monitoring, detection, image analysis, segmentation

References

1. Tarancev A.A., Chikitov Yu.I. Optimizaciya chisla BPLA dlya monitoringa pozharov krupnyh lesnyh massivov // Problemy upravleniya riskami v tehnosfere. 2015. № 3 (35). S. 3-9.

2. Rui V.A., Pedro V. Forest Fire Finder - DOAS application to long-range forest fire detection // Published by Copernicus Publications on behalf of the European Geosciences Union. Atmos. Meas. Tech. 2017. 10. Pp. 2 299-2 311.

3. Zuoning W., Pengfei L., Tiejun C. Research on forest flame recognition algorithm based on image feature // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XLII-2/W7, 2017. ISPRS Geospatial Week 2017, 18-22 September, 2017. Pp. 925-928.

4. WANG Yu-bin. Study on fire smoke detection technology based on image type [J] // Fire Science and Technology. 2014. 33 (9). Pp. 1 052-1 055.

5. Komashinskiy V.I., Smirnov D.A. Neyronnye seti i ih primenenie v sistemah upravleniya i svyazi. M., 2003.

6. Malygin I.G., Komashinskiy V.I., Ivanov A.Yu. Koncepciya postroeniya edinogo informacionnogo prostranstva intellektual'noy mul'timodal'noy transportnoy sistemy // Transport Rossiyskoy Federacii. 2016. № 6 (67). S. 24-28.

7. Sistematizaciya algoritmov nahozhdeniya i kodirovaniya opornyh tochek izobrazheniy / Sh.S. Fahmi [i dr.] // Voprosy radioelektroniki. Ser.: Tehnika televideniya. 2017. Vyp. 3. S. 15-20.

8. Videosistemy na kristalle selekcii ob'ektov na primere obnaruzheniya lesnyh pozharov / Sh.S. Fahmi [i dr.] // Televidenie: peredacha i obrabotka izobrazheniy: materialy XIV Mezhdunar. nauch.-tehn. konf. SPb.: SPbGETU «LETI», 2017. S. 106-113.

9. Fahmi Sh.S., Alekseenko Ya.V. Ispol'zovanie geoinformacionnyh sistem kosmicheskogo monitoringa MChS Rossii v pozharoopasnyy period // Aktual'nye problemy sozdaniya kosmicheskih sistem distancionnogo zondirovaniya zemli: tezisy dokladov IV Mezhdunar. nauch.-tehn. konf. M.: AO «Korporaciya «VNIIEM», 2016. S. 155-156.

10. Gonsales R., Vuds R. Cifrovaya obrabotka izobrazheniy. M.: Tehnosfera, 2012. 1 104 s.

11. Weakly-and semi-supervised learning of a DCNN for semantic image segmentation[J] / G. Papandreou [et al]. arXiv preprint arXiv:1502.02734, 2015.

12. Geoservis po lesnym pozharam «Kosmosnimki». URL: http://fires.kosmosnimki.ru (data obrascheniya: 05.02.2018).

13. Sistematizaciya algoritmov nahozhdeniya i kodirovaniya opornyh tochek izobrazheniy / Sh.S. Fahmi [i dr.] // Voprosy radioelektroniki. Ser.: Tehnika televideniya. 2017. Vyp. 3. S. 15-20.

14. Zhong Ma.A., Zhang Y.L. Adaptive Multiobjective Memetic Fuzzy Clustering Algorithm for Remote Sensing Imagery. IEEE Trans. Geosci. Remote Sens. 2015. 53. P. 4 202-4 217.

15. Somanath A., Karaman S., Youcef-Toumi K. Controlling stochastic growth processes on lattices: Wildfire management with robotic fire extinguishers // In 53rd IEEE Conference on Decision and Control. 2014. Dec. Pp. 1 432-1 437.

16. Gulyaev P.Yu., Dolmatov A.V., Iordan V.I. Adaptivnyy algoritm obucheniya bayesovskogo neyrosetevogo klassifikatora informacionnyh klasternyh struktur v spektrozonal'nyh izobrazheniyah // Neyroinformatika i ee prilozheniya: materialy XIII Vseros. seminara / pod red. A.N. Gorbanya, E.M. Merkesa. Krasnoyarsk: IVM SO RAN, 2005. S. 29-30.

17. Beloglazov I.N., Dzhandzhgava G.I., Chigin G.P. Osnovy navigacii po geofizicheskim polyam. M.: Nauka, 1985.

18. Praveenchakkaravarthy S., Nancy J., NaveenKumar V.S., NeethiNarayanan, Pavithra R. Forest fire detection system // International Journal of Recent Trends in Engineering. 2017. Pp. 99-102.

19. MODIS. URL: https://modis.gsfc.nasa.gov/news/ (data obrascheniya: 02.04.2018).

Submit manuscript

To cite

Citations:

Confirmation

Регистрация