Deep Learning model for wildfire detection through the fusion of visible and infrared information

Early wildfire detection is of vital importance to prevent the damage caused by wildfires to both the environment and human beings. We propose a Deep Learning (DL) model that leverages the information fusion of visible and infrared images for accurate wildfire detection in controlled datasets; we expect the said model to display a lower rate of false-positives in comparison with current techniques. To this end, it is necessary to first investigate, analyze, and test existing early wildfire detection and image fusion methods. Additionally, we will create a dataset comprised of fused visible-infrared images. In the present paper, we introduce the proposed approach and some preliminary results regarding the evaluation of two state-of-the-art image fusion techniques on the Corsican Fire Dataset, as well as advances towards the fused image dataset generation.