Temperature compensation method of UAV thermal infrared monitoring in shallow buried fire area

The northwestern region in China has the characteristics of large coal seam thickness and shallow burial depth. Unmanned aerial vehicle (UAV) thermal infrared monitoring and temperature compensation are key technologies for monitoring shallow coal fire disasters, which is of great significance for advancing the safety monitoring and warning evaluation of coal spontaneous combustion disasters. A temperature compensation model based on the grey wolf optimization-double-layer generalized regression neural network (GWO-double-layer GRNN) and continuous altitude correction function is proposed to address the influence of multiple parameters on thermal infrared temperature monitoring results in complex environments. The factors affecting the temperature results under the thermal infrared monitoring band are selected based on the contrast of the atmospheric extinction coefficient to the received radiation, and the index factors that can cumulatively characterize the actual temperature are determined by principal component analysis. The index factor data from the UAV thermal infrared monitoring experiments in various environments are used as inputs to the GWO-double-layer GRNN network to obtain a discretely trained temperature compensation model. A continuous correction function for UAV altitude is proposed as a pre-processing step for the model input to compensate for the data, and the complete temperature compensation model is tested and field-validated. The results show that the discrete compensation effect of the GWO-double-layer GRNN network is superior to other models in data testing, with MAE ≤ 0.008 1, RMSE ≤ 0.013 2, and R² ≥ 0.996 9, indicating that the model has good compensation effects. The continuous correction function avoids the influence of UAV altitude on thermal infrared monitoring results, dividing the problem of UAV continuous altitude thermal infrared temperature regression into a stepwise regression calculation, and the final model has a good monitoring accuracy, improving the generalization ability of the temperature compensation model. It provides a supporting calculation method for applying UAV thermal infrared monitoring results to delineate shallow coal fire hazard areas and further promote this method to the corresponding UAV applications and laser monitoring industry.