## 1 Introduction  
Wildfires represent a considerable threat, as they can have a significant and negative impact on the environment, properties, and lives. In 2020, hundreds of fires were registered across Northern California. They were the largest fires in California's history, with a total of 1.03 million acres burned [1]. In the United States alone, an estimated 17,904 structures burned in wildfires in 2020, most of them in California [2]. Currently, there are three main categories of forest fire remote monitoring and detection techniques: ground-based systems, manned aerial vehicle-based systems, and satellite-based systems. These techniques present several disadvantages: ground-based systems display limited operation ranges, while satellite-based systems lack path planning flexibility, and manned aerial vehicle-based systems are expensive and potentially dangerous for their operators [3, 4]. Additionally, sensor-based fire detection can display false alarms, in addition to the high costs associated with the installation of multiple sensors across large areas [5]. In contrast, unmanned aerial vehicles (UAVs) with computer vision-based sensing systems provide a flexible, low-cost alternative [3, 4]. In the field of computer vision-based fire detection, different algorithms to detect fire in video or image sequences have been proposed [6; 7; 8; 9; 10]. However, the results of these algorithms refer only to the presence of fire on an image, that is, classifying an image as either fire or non-fire. These techniques are thus not adequate to perform a precise segmentation of the fire [11], that is, the detection of fire pixels in an image. Fire segmentation is of great interest as it represents the first step of several processing stages for both the detection of fire departure and the monitoring and modeling of the fire [5]. The segmentation of fire areas in an image allows us to obtain relevant information regarding its position, rate of spread, height, inclination, surface, and volume [12]. Visible images contain textural details with a high spatial resolution that are consistent with the human visual system. In the visible spectrum, the performance of the segmentation process can vary depending on the color and the texture of the fire coupled with the presence of varying quantities of black or white smoke [11]. In contrast, the presence of fire in infrared images is more distinguishable thanks to large temperature differences with the background [13]. However, it is also not trivial to detect fire on infrared images, as they present problems such as thermal reflections and infrared (IR) blocking [14]. Thus, the fusion of both the textural and thermal information in a single image has the potential to increase the segmentation efficiency, potentially improving the robustness, accuracy, and reliability of fire segmentation systems [3]. In recent years, Deep Learning (DL) has displayed state-of-the-art performance in different tasks such as image classification [15; 16; 17], object detection [18; 19; 20], and image segmentation [21; 22; 23]. DL is an area of Machine Learning (ML) based on artificial neural networks, such as convolutional neural networks (CNNs), that represents a statistical technique for classifying patterns based on sample or training data using multi-layer neural networks [24]. Most DL algorithms consist of a hierarchical architecture with multiple layers; each layer constitutes a non-linear information processing unit [25]. Additionally, in image segmentation, a relevant element in a DL model is the loss function (for the supervised learning case). The loss function evaluates how well the predicted segmentation matches its corresponding ground truth. The latter is a necessary step in the training of a DL model [26]. There exist several approaches for visible-infrared image fusion in the state-of-the-art, both with traditional image processing techniques [4; 27; 28] and with DL-based methods [29; 30; 31]. In the particular context of fire imagery, [PERSON] et al.