2021
DOI: 10.1002/cpe.6280
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Forest fire detection on LANDSAT images using support vector machine

Abstract: In recent days, the major threat in the world is forest fire that affects the biodiversity, climate change, and so forth. So detection process is more essential to monitor the forest region. To detect the forest fire, the paper proposes a novel detection technique of support vector machine (SVM)-Krill herd that can effectively detect the fire region using different kinds of features. Land surface temperature, fire intensity, water vapor, and top of atmosphere temperature are being extracted as some of the feat… Show more

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Cited by 10 publications
(2 citation statements)
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“…In a similar vein, an analysis of forest fire detection systems by Barmpoutis et al (2020) and 9), but are limited to a temporal resolution of 16 days (Acharya and Yang, 2015;Chanthiya and Kalaivani, 2021;Fu et al, 2020). However, new developments of real time detection and life tracking of wildfires based on a set of over 20 satellites such as provided by OroraTech (noa, 2021) show the potential of future analysis of forest fires.…”
Section: Shortcomings and Future Perspectivesmentioning
confidence: 99%
“…In a similar vein, an analysis of forest fire detection systems by Barmpoutis et al (2020) and 9), but are limited to a temporal resolution of 16 days (Acharya and Yang, 2015;Chanthiya and Kalaivani, 2021;Fu et al, 2020). However, new developments of real time detection and life tracking of wildfires based on a set of over 20 satellites such as provided by OroraTech (noa, 2021) show the potential of future analysis of forest fires.…”
Section: Shortcomings and Future Perspectivesmentioning
confidence: 99%
“…Additionally, edges, textures, and other features have also been used for fire detection [15][16][17][18][19]. Detection accuracy and computation speed have been further improved using machine learning algorithms, such as Decision Trees and Bayesian networks, Support Vector Machines, and so on [20,21]. According to the manually selected characteristics, these methods have achieved good detection results but struggle with nuisance alarms caused by interference factors in practical applications, such as light, clouds, chimney emissions, etc.…”
Section: Introductionmentioning
confidence: 99%