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.