Infra-red line camera data-driven edge detector in UAV forest fire monitoring

Abstract

The accurate prediction of the wildfire spread-rate is a challenging task, due to the high number of parameters involved and the underlying complex dynamic multi-physics processes which drive the phenomenon. For these reasons, data-driven prediction tools could be useful to provide a more accurate prediction of the fire front. In this scenario, systematic fire data gathering becomes crucial and using an Unmanned Aircraft Vehicle (UAV) is strategic to reduce considerably the risk associated with flying a manned aircraft into low visibility and extremely turbulent air, sustained by the fire-induced convective motions.Moreover the employment of the UAV is beneficial, as the possibility of flying at very low altitudes maximizes the on-board Electro-Optical (EO) sensor effectiveness. The aim is to develop a real time data-driven fire propagator to support wildfire fighting operations and to facilitate the risk assessment and decision making process. In order to collect data, the fire front position has to be measured using an infra-red (IR) camera so as to overcome the limitations associated to a visible camera in low visibility (smoky)conditions and night operations. To reduce the computational cost associated to the image processing, a Line Camera (LC) configuration has been preferred. Because of the mono-dimensionality of the measure, classical edge detector, like the Canny method, or contour algorithms, developed for 2D images, can not be applied. In this paper, a mono-dimensional noise-resistant algorithm for edge detection is presented. The generality of the proposed method opens the possibility to a variety of heterogeneous problems of different nature. The robustness of this algorithm resides in the use of known physical characteristics of the target of interest, to increase the feature edge discontinuity. Its straightforwardness guarantees fast computation, making it very attractive for real time image processing, remote sensing applications and for UAV surveillance tasks.