Fuzzy Rule-Based Combination Model for the Fire Pixel Segmentation
Journal
IEEE Access
ISSN
2169-3536
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Date Issued
2025
Author(s)
Lopez-Alanis, Alberto
de-la-Torre-Gutierrez, Hector
Hernández-Aguirre, Arturo
Type
journal-article
Abstract
Color-feature-based wildfire pixel segmentation has become a challenging task extensively addressed in various research studies. Rule-based models aim to identify fire pixels in a binary manner by determining whether the pixel intensity exceeds a specified threshold value. The authors determine the thresholds by analyzing diverse collections of images that contain wildfires. This has resulted in a lack of consensus on the thresholds determined by various researchers, even when the same color space is used during the examination process. Additionally, determining fire pixels in a binary manner complicates the handling of uncertainty and vagueness in color information. This research aims to enhance fire-pixel segmentation by integrating color-based rule models with a fuzzy set approach, which effectively addresses uncertainty and vagueness. The proposed approach automatically learns the optimal set of fuzzy operators and rules for fire detection to construct a combined model. To address the limitations of combining binary class labels, this approach modifies the rule form proposed by various authors to obtain a fuzzy set of data, such as a grayscale fire map, instead of a crisp set of data, such as a binary fire map. In addition, our proposal uses a genetic algorithm approach to construct the best combination model. The final binary form of the fire map is calculated using the widely used Otsu method. The presented method is evaluated qualitatively and quantitatively in a well-accepted dataset designed for wildfire pixel segmentation tasks. The model obtained outperforms state-of-the-art rules and traditional strategies for combining binary labels in the F-measure and IoU metrics. © 2013 IEEE.
License
Open access
How to cite
A. Lopez-Alanis, H. de-la-Torre-Gutierrez, A. Hernández-Aguirre and M. T. Orvañanos-Guerrero, "Fuzzy Rule-Based Combination Model for the Fire Pixel Segmentation," in IEEE Access, vol. 13, pp. 52478-52496, 2025, doi: 10.1109/ACCESS.2025.3554140. keywords: {Image color analysis;Image segmentation;Computational modeling;Proposals;Wildfires;Computer vision;Uncertainty;Genetic algorithms;Convolutional neural networks;Fuzzy sets;Fire-pixel segmentation;fuzzy rules;genetic algorithm;color-based rule model;combination model.