Coupling Physical Model and Deep Learning for Near Real-Time Wildfire Detection

Accurate and timely monitoring of wildfires is crucial for reducing property damage and casualties. In recent years, advances in satellite technology have enabled the comprehensive, timely, and rapid recording of various abrupt events on the Earth's surface. However, achieving a balance between temporal and spatial resolution remains a significant challenge for remote sensing, hindering the quick and accurate detection of wildfires. This letter proposes a novel framework for the near real-time monitoring of wildfire coupled with the bidirectional reflectance distribution function (BRDF) model and deep learning technology, which enables near real-time detection of wildfire by assessing the degree to which the observed value of geostationary satellite image deviates from the predicted theoretical observation value. The experimental results show that the proposed method is capable of effectively detecting wildfires in near real-time. Moreover, the encouraging results suggest that the method holds promise for monitoring the spread of wildfire to a certain extent.