A Simple Neural-network Algorithm for Classification of Lidar Signals Applied to Forest-fire Detection

Detection of smoke plumes using lidar provides many advantages with respect to passive methods of fire surveillance. However, the great sensitivity of the method results in the detection of many spurious signals. Correspondingly, the automatic lidar surveillance must be provided with effective algorithms of separation of the smoke-plume signatures from irrelevant signals. The paper discusses a simple and robust lidar pattern recognition procedure based on the fast extraction of sufficiently pronounced signal peaks and their classification with a perceptron, whose efficiency is enhanced by a fast nonlinear preprocessing. The algorithm is benchmarked against previously developed artificial-intelligence methods of smoke recognition via Relative Operating Characteristic (ROC curve) analysis.