Abstract
The perimeter of a wildfire is essential for prediction of the spread of a wildfire. Real-time information on an active wildfire can be obtained with Thermal InfraRed (TIR) data collected via aerial surveys or satellite imaging, but often lack the actual numerical parametrization of the wildfire perimeter. As such, additional image processing is needed to formulate closed polygons that provide the numerical parametrization of wildfire perimeters. Although a traditional image segmentation method (ISM) that relies on image gradient or image continuity can be used to process a TIR image, these methods may fail to accurately represent a perimeter or boundary of an object when pixels representing high infrared values are sparse and not connected. An ISM processed TIR image with sparse high infrared pixels often results in multiple disconnected sub-objects rather than a complete object. This paper solves the problem of detecting wildfire perimeters from TIR images in three distinct image processing steps. First, Delaunay triangulation is used to connect the sparse and disconnected high-value infrared pixels. Subsequently, a closed (convex) polygon is created by joining adjacent triangles. The final step consists of an iterative trimming method that removes redundant triangles to find the closed (non-convex) polygon that parametrizes the wildfire perimeter. The method is illustrated on a typical satellite TIR image of a wildfire, and the result is compared to those obtained by traditional ISMs. The illustration shows that the three image processing steps summarized in this paper yield an accurate result for representation of the wildfire perimeter.
Work is supported by WIFIRE Commons and funded by NSF 2040676 and NSF 2134904 under the Convergence Accelerator program.
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
Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1485–1490 (2005)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient nd image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)
Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In: Proceedings of the Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 1, pp. 105–112. IEEE (2001)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)
Chiaraviglio, N., et al.: Automatic fire perimeter determination using MODIS hotspots information. In: 2016 IEEE 12th International Conference on e-Science (e-Science), pp. 414–423 (2016)
Delaunay, B., et al.: Sur la sphere vide. Izv. Akad. Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk 7(793–800), 1–2 (1934)
Ding, L., Goshtasby, A.: On the canny edge detector. Pattern Recogn. 34(3), 721–725 (2001)
Evensen, G.: The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn. 53(4), 343–367 (2003)
Fang, H., Srivas, T., de Callafon, R.A., Haile, M.A.: Ensemble-based simultaneous input and state estimation for nonlinear dynamic systems with application to wildfire data assimilation. Control Eng. Pract. 63, 104–115 (2017)
Finney, M.A.: FARSITE, Fire Area Simulator-model development and evaluation. No. 4, US Department of Agriculture, Forest Service, Rocky Mountain Research Station (1998)
Engquist, B. (ed.): Encyclopedia of Applied and Computational Mathematics. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-540-70529-1
Jain, P., Coogan, S.C., Subramanian, S.G., Crowley, M., Taylor, S., Flannigan, M.D.: A review of machine learning applications in wildfire science and management. arXiv preprint arXiv:2003.00646 (2020)
Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)
Mandel, J., et al.: Towards a dynamic data driven application system for wildfire simulation. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2005. LNCS, vol. 3515, pp. 632–639. Springer, Heidelberg (2005). https://doi.org/10.1007/11428848_82
Mumford, D., Shah, J.: Boundary detection by minimizing functionals. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 17, pp. 137–154. San Francisco (1985)
Ononye, A.E., Vodacek, A., Saber, E.: Automated extraction of fire line parameters from multispectral infrared images. Remote Sens. Environ. 108(2), 179–188 (2007)
Rong, W., Li, Z., Zhang, W., Sun, L.: An improved canny edge detection algorithm. In: 2014 IEEE International Conference on Mechatronics and Automation, pp. 577–582. IEEE (2014)
Schroeder, W., Oliva, P., Giglio, L., Quayle, B., Lorenz, E., Morelli, F.: Active fire detection using landsat-8/oli data. Remote Sens. Environ. 185, 210–220 (2016). landsat 8 Science Results
Srivas, T., Artés, T., de Callafon, R.A., Altintas, I.: Wildfire spread prediction and assimilation for FARSITE using ensemble Kalman filtering. Procedia Comput. Sci. 80, 897–908 (2016)
Subramanian, A., Tan, L., de Callafon, R.A., Crawl, D., Altintas, I.: Recursive updates of wildfire perimeters using barrier points and ensemble Kalman filtering. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12142, pp. 225–236. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50433-5_18
Toulouse, T., Rossi, L., Campana, A., Celik, T., Akhloufi, M.A.: Computer vision for wildfire research: an evolving image dataset for processing and analysis. Fire Saf. J. 92, 188–194 (2017)
Valero, M.M., Rios, O., Mata, C., Pastor, E., Planas, E.: An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires. Fire Saf. J. 91, 835–844 (2017). fire Safety Science: Proceedings of the 12th International Symposium
Valero, M., Rios, O., Pastor, E., Planas, E.: Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors. Int. J. Wildland Fire 27(4), 241–256 (2018)
Yi, F., Moon, I.: Image segmentation: a survey of graph-cut methods. In: 2012 International Conference on Systems and Informatics (ICSAI2012), pp. 1936–1941. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tan, L., Hu, Y., Tan, S., de Callafon, R.A., Altıntaş, I. (2023). Wildfire Perimeter Detection via Iterative Trimming Method. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14073. Springer, Cham. https://doi.org/10.1007/978-3-031-35995-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-35995-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35994-1
Online ISBN: 978-3-031-35995-8
eBook Packages: Computer ScienceComputer Science (R0)