Fuzzy Rule-Based Combination Model for the Fire Pixel Segmentation

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.