Forest fire identification method of UAV remote sensing image based on FCM clustering algorithm

In order to overcome the problems of low recognition accuracy and speed in traditional forest fire recognition methods, the paper proposes a forest fire recognition method based on unmanned aerial vehicle remote sensing images using FCM clustering algorithm. Firstly, the FCM clustering algorithm is used to cluster and segment the target RGB pixels in unmanned aerial vehicle remote sensing images. Secondly, according to the calculation rules of the LPB algorithm, the flame characteristics of forest fires are calculated. Finally, the optimal hyperplane of SVM is used to judge whether the target RGB pixels in the remote sensing image are fire pixels, and the forest fire recognition method can be obtained after traversing all pixels. The experimental results show that the fire location identified by this method is completely consistent with the actual situation, and the recognition rate can reach a maximum of 52 frames/s.

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Publication Details

Journal
International Journal of Reasoning-based Intelligent Systems
Published
2025-01-01
DOI
https://doi.org/10.1504/ijris.2025.146936
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
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article

Forest fire identification method of UAV remote sensing image based on FCM clustering algorithm

Wei He, Yuqing Tan, Haifeng Zhang, Peiran Li et al.
International Journal of Reasoning-based Intelligent Systems
Remote Sensing and Land Use
article

Forest fire identification method of UAV remote sensing image based on FCM clustering algorithm

Wei He, Yuqing Tan, Haifeng Zhang, Peiran Li, Zhanlan Xie
article en

Abstract

In order to overcome the problems of low recognition accuracy and speed in traditional forest fire recognition methods, the paper proposes a forest fire recognition method based on unmanned aerial vehicle remote sensing images using FCM clustering algorithm. Firstly, the FCM clustering algorithm is used to cluster and segment the target RGB pixels in unmanned aerial vehicle remote sensing images. Secondly, according to the calculation rules of the LPB algorithm, the flame characteristics of forest fires are calculated. Finally, the optimal hyperplane of SVM is used to judge whether the target RGB pixels in the remote sensing image are fire pixels, and the forest fire recognition method can be obtained after traversing all pixels. The experimental results show that the fire location identified by this method is completely consistent with the actual situation, and the recognition rate can reach a maximum of 52 frames/s.

International Journal of Reasoning-based Intelligent SystemsVol. 17(2)
State Grid Corporation of China (China) (CN), Shanghai Electric (China) (CN), Ningxia Water Conservancy (CN)
Climate action
Openalex Percentile: Top 80%
Remote Sensing and Land Use
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