Title: SMOKE AND FOG CLASSIFICATION IN FOREST MONITORING USING HIGH SPATIAL RESOLUTION IMAGES
Peer-reviewed articles 17,970 +

ARTICLE METRICS


Altmetrics info

Title: SMOKE AND FOG CLASSIFICATION IN FOREST MONITORING USING HIGH SPATIAL RESOLUTION IMAGES

SMOKE AND FOG CLASSIFICATION IN FOREST MONITORING USING HIGH SPATIAL RESOLUTION IMAGES
Julia Ahlen
10.5593/sgem2022/2.1/s08.16
10.5593/sgem2022/2.1
1314-2704
978-619-7603-40-8
English
22
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Geoinformatics
Forest fires cause major damage to human habitats and forest ecosystems. Early detection may prevent serious consequences of fast fire spread. Although there are many smoke detection algorithms employed by various optical remote sensing systems, there is still a major misdetection of images containing fog. Fog exhibits similar visual characteristics to smoke. Furthermore, when monitoring dense forests many smoke detection algorithms would fail in robust recognition due to fog covering the trees at dawn. There have been more or less successful attempts to separate smoke from a fog in optical imagery however, these algorithms are strongly connected to a specific application area or use a semiautomatic approach. This work aims to propose a novel smoke and fog separation algorithm based on color space model calculation followed by rule-based shape analysis. In addition, the internal properties of the smoke candidate areas are examined for linear attenuation towards higher energy wavelength. Those areas are then investigated for internal shape properties such as convex hull and eccentricity. Several tests conducted on various high-resolution aerial images suggest that the system is effective in differentiating smoke and fog and thus considered to be robust in early fire detection in forest areas.
smoke, fog, detection, image, rule-based
[1] Yue, X., Mickley, L. J., Logan, J. A., Hudman, R. C., Martin, M. V., and Yantosca, R. M.: Impact of 2050 climate change on North American wildfire: consequences for ozone air quality, Atmos. Chem. Phys., 15, pp. 10033–10055, https://doi.org/10.5194/acp-15-10033-2015, 2015.
[2] Cunillera-Montcusi, D, Gascon, S, Tornero, I, et al. Direct and indirect impacts of wildfire on faunal communities of Mediterranean temporary ponds. Freshw Biol. 2019; 64: pp. 323– 334. https://doi.org/10.1111/fwb.13219.
[3] Barmpoutis, P.; Papaioannou, P.; Dimitropoulos, K.; Grammalidis, N. A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing. Sensors 2020, 20(22), 6442; https://doi.org/10.3390/s20226442.
[4] Ruwaimana, M. & Satyanarayana, B. & Otero, V. & M Muslim, A. & A., Muhammad & Ibrahim., S. & Raymaekers, D. & Koedam, N. & Dahdouh-Guebas, F. The advantages of using drones over space-borne imagery in the mapping of mangrove forests. PLOS ONE. 13. e0200288. 10.1371/journal.pone.0200288, 2018.
[5] Yumnam K. S., Smoke Region Detection from a Single Color Image, International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356, www.ijres.org Volume 9 Issue 7 pp. 64-70, 2021.
[6] F, Freceena. Smoke and fog Detection in Images. International Journal of Computer Sciences and Engineering. 06. 2018, pp. 54-57. 10.26438/ijcse/v6si6.5457.
[7] Govil, K.; Welch, M.L.; Ball, J.T.; Pennypacker, C.R. Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images. Remote Sens. 2020, 12, 166. https://doi.org/10.3390/rs12010166
[8] Ozbek, M and Y?ld?z, U. Smoke detection from foggy environment based on color spaces International Journal of Applied Mathematics Electronics and Computers 09(03): 072-078, 2021, pp. 72-78.
[9] Dimitropoulos, K.; Tsalakanidou, F.; Grammalidis, N. Flame detection for videobased early fire warning systems and 3D visualization of fire propagation. In Proceedings of the 13th IASTED International Conference on Computer Graphics and Imaging (CGIM 2012), Crete, Greece, 18–20 June 2012. Available online: https://zenodo.org/record/1218#.X6qSVmj7Sbg (accessed on 10 November 2020).
[10] Lin, L.; Meng, Y.; Yue, A.; Yuan, Y.; Liu, X.; Chen, J.; Zhang, M.; Chen, J. A spatiotemporal model for forest fire detection using HJ-IRS satellite data. Remote Sens. 2016, 8, 403.
[11] F.M. AnimHossain, Youmin M.Zhang, and Masuda AkterTonima. Forest fire flame and smoke detection from UAV-captured images using fire-specific color features and multi-color space local binary pattern. Journal of Unmanned Vehicle Systems. 8(4): 285- 309. https://doi.org/10.1139/juvs-2020-0009
[12] H. Tian, W. Li, P. O. Ogunbona and L. Wang, "Detection and Separation of Smoke From Single Image Frames," in IEEE Transactions on Image Processing, vol. 27, no. 3, pp. 1164-1177, March 2018, doi: 10.1109/TIP.2017.2771499.
[13] I, Muhammad & L. Minh, H. Rajbhandari, S. & P., Joaquin. Analysis of Fog and Smoke Attenuation in a Free Space Optical Communication Link under Controlled Laboratory Conditions. 2012, 10.1109/IWOW.2012.6349680
This work is funded by Geospatial Information Science Group at the University of Gavle, Sweden.
conference
Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 04 - 10 July, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
131-138
04 - 10 July, 2022
website
8483