Abstract
The current concern over wildfires stems from their devastating impact on ecosystems, wildlife and human lives. These fires have adverse effects on the environment, resulting in the destruction of vegetation cover, loss of biodiversity and environmental degradation. Detecting wildfires in a timely manner allows for prompt response, mitigating environmental damage and ensuring the safety of individuals, particularly those residing near forested areas. There are various methods available for wildfire detection, including the use of sensor networks, employing artificial intelligence and machine learning for fire prediction and satellite image observation, which is a prominent focus of researchers. In this study, we introduce several fuzzy clustering methods and their applications for detecting wildfire in satellite images. The experimental results demonstrate the effectiveness of these methods in identifying wildfire areas within satellite imagery. Notably, the Picture Fuzzy Clustering method exhibits high clustering efficiency and provides clear images for wildfire detection. This approach proves valuable for disaster prevention and wildfire detection, enabling swift decision-making and implementation of solutions to mitigate potential harm to human lives, properties and ecosystems.
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Huan, P.T., Canh, H.T., Thai, V.D., Khoi, D.H., Giang, L.T. (2023). Enhancing Wildfire Detection Using Semi-supervised Fuzzy Clustering on Satellite Imagery. In: Nghia, P.T., Thai, V.D., Thuy, N.T., Son, L.H., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2023. Lecture Notes in Networks and Systems, vol 847. Springer, Cham. https://doi.org/10.1007/978-3-031-49529-8_18
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