NRBO-XGBoost-Optimized High-Fidelity Temperature Correction for UAV-Based TIR Imagery and Its Application for Monitoring Coal Fire

To mitigate the limitations of low measurement accuracy and substantial environmental interference in UAV-based TIR imaging for coal fire monitoring, this study presents an integrated temperature correction approach, termed NRBO-XGBoost. The proposed method applies temperature correction to TIR imagery and subsequently investigates coal fire detection using the corrected TIR data. By leveraging multi-source data (thermal infrared measurements, UAV flight altitude, and meteorological parameters), the NRBO optimizes XGBoost hyperparameters to improve model convergence speed and global search capability, effectively overcoming the limitations of traditional methods, such as local optima entrapment and poor generalization. Experimental results demonstrate that the NRBO-XGBoost model achieves superior performance in temperature correction, with a coefficient of determination (R2) of 0.9993, while reducing RMSE and MAE by 85.6% and 86.6%, respectively. Notably, the model exhibits enhanced stability in high-temperature regions (>300 °C). The 3D reconstruction results demonstrate a nearly 6-fold expansion in high-temperature area coverage (from 0.43% to 2.60%), coupled with a morphological transformation of fragmented hotspots into continuous, belt-shaped distributions. Integrating visible-light textures further improves boundary clarity and spatial semantic representation of thermal anomalies. This study provides a high-precision temperature correction and 3D visualization solution for coal fire monitoring, offering critical technical support for early warning systems and firefighting strategies.