Spatial Chromatic Instability: A Lightweight Feature Extraction Technique for Wildfire Detection

Spatial chromatic instability is currently one of the most robust methods for improving solutions proposed for image-based fire detection systems. Real flames exhibit erratic, turbulent local color variations, providing a more reliable discriminative signal than global color information alone, especially in visually ambiguous non-fire situations. This study proposes a generalizable feature representation based on the Spatial Chromatic Instability Index (ICCS) to measure local RGB variations (ICCSR, ICCSG, ICCSB, and ICCST). Two public datasets comprising both fire image files and non-fire imagery were used. The Hilbert–Schmidt Independence Criterion (HSIC) and Silhouette coefficient analysis were used to quantify the statistical dependence between feature sets and the resulting cluster separation. To evaluate the practical discriminatory performance of spatial chromatic instability, three classifiers, i.e., Logistic Regression, Linear SVM, and Random Forest, were employed. To verify the proposed approach’s effectiveness, three deep learning models, Swin Transformer, MobileViT, and ViT-Base-16, were also employed for cross-checking. Performance metrics demonstrated that integrating ICCS features into global color features improved classification. Logistic Regression performed best overall on the Kaggle dataset when local ICCS features were included, achieving an accuracy of 0.935 and an F1-score of 0.958. For the Mendeley dataset, Linear SVM achieved an accuracy of 0.862 and an F1-score of 0.881. The ICCS is a robust, easy-to-understand, and fast approach for identifying fires. It has real potential in early warning systems, mainly due to its limited requirements for computing power.