A stochastic model for active fire detection using the thermal bands of MODIS data

Active fire detection using satellite thermal sensors usually involves thresholding the detected brightness temperature in several bands. Most frequently used features for fire detection are the brightness temperature in the 4-micron wavelength band (T4) and the brightness temperature difference between the 4-micron and 11-micron bands (dT=T4-T11). In this paper, the task of active fire detection is examined in the context of a stochastic model for target detection. Probability density functions for the fire and background pixels are computed in the (T4, dT) feature space using simulated MODIS thermal data under different atmospheric humidity conditions and for cases of flaming and smoldering fires. The Pareto curve for each detection case is constructed. Optimal thresholds are derived by minimizing a cost function, which is a weighted sum of the omission and commission errors