Development of neural network committee machines for automatic forest fire detection using lidar

Lidar has considerable potential as an early forest fire detection technique, presenting considerable advantages when compared to the passive detection methods based on infrared cameras currently in common use, due to its higher sensitivity, ability to accurately locate the fire and the fact that it does not need line of sight to the flames. The method has recently been demonstrated by the authors, but its automation requires the availability of a rapid signal analysis technique, for prompt alarm emission whenever required. In the present paper a novel method of classifying lidar signals using committee machines composed of neural networks is proposed. A new method based on ROC curves and the Neyman-Pearson criterion is used to choose the optimal number of training epochs for each neural network in order to avoid overfitting. The best committee machine, obtained on the basis of these principles and selected to lead to the lowest percentage of false alarms for a true detection percentage of 90% for a test set created by adding random noise to patterns obtained experimentally, was composed of three single-layer perceptrons and presented a true detection efficiency of 94.4% and 0.553% of false alarms in the validation set. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.