Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles

Abstract

Due to their fast response capability, low cost and without danger to personnel safety since there is no human pilot on-board, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring and detecting forest fires. This paper proposes a novel forest fire detection method using both color and motion features for processing images captured from the camera mounted on a UAV which is moving during the whole mission period. First, a color-based fire detection algorithm with light computational demand is designed to extract fire-colored pixels as fire candidate regions by making use of chromatic feature of fire and obtaining fire candidate regions for further analysis. As the pose variations and low-frequency vibrations of UAV cause all objects and background in the images are moving, it is challenging to identify fires defending on a single motion based method. Two types of optical flow algorithms, a classical optical flow algorithm and an optimal mass transport optical flow algorithm, are then combined to compute motion vectors of the fire candidate regions. Fires are thereby expected to be distinguished from other fire analogues based on their motion features. Several groups of experiments are conducted to validate that the proposed method can effectively extract and track fire pixels in aerial video sequences. The good performance is anticipated to significantly improve the accuracy of forest fire detection and reduce false alarm rates without increasing much computation efforts.