## 3 Materials and Methods
### Image Fusion Methods
#### 3.2.2 Fire-GAN  
A Novel Deep Learning-Based Infrared-Visible Fusion Method for Wildfire Imagery  
This method, proposed by [PERSON] et al. [34] in 2021, presents a GAN-based approach that builds upon the one proposed by [PERSON] et al. [31] in 2020 for the particular context of fire imagery. The _Fire-GAN_ model takes as an input a visible image, generating an artificial infrared one and then fusing these two to produce the final output image. It is relevant to note that the kind of infrared images, that is, NIR, short-wavelength (SWIR), mid-wavelength (MWIR), or long-wavelength (LWIR), that the model learns to generate is dependent on the source infrared images present in the training set. This means that if the training set contains NIR images, this method generates approximate NIR images, and so forth. For the case of the present paper, the model learns to generate approximate NIR images.  
First, the visible image is given as an input to a U-Net-like generator \(G1\) to create an approximate NIR image. Then, the two (approximate NIR and visible) are concatenated and  
Figure 4: Framework of the _VGG19_ proposed by [PERSON] et al. [29].  
fed to a second generator \(G2\), generating an RGB fused image as output. A discriminator \(D1\) has the task of distinguishing between the source visible image and the generated fused one. The latter motivates \(G2\) to include more textural details, thus making the fused image closer to the visible one. At the same time, a second discriminator \(D2\) seeks to distinguish between the source NIR image, the approximate one, and the final fused image, thus encouraging \(G1\) to output more accurate NIR images and also allowing \(G2\) to incorporate more thermal information in the final fused image. Finally, due to the particular thermal characteristics of the fire NIR images, the authors include a constant \(\gamma\) term in the loss function of \(G2\) to control and prioritize, as needed, the inclusion of visible information. Figure 5 shows the structure of the _Fire-GAN_ model with sample images from the Corsican Fire Database.  
This model is, to the best of our knowledge, the first one to address DL-based visible-infrared fusion of fire imagery. It has the advantage of being end-to-end, which significantly reduces its implementation complexity. It is capable of preserving color information in the fused images, which can potentially aid in the segmentation of the fire.  
Additionally, it is worth noting that the fusion process requires perfectly aligned source images [13]. For the particular context of fire images, the generation of artificial NIR images presents an advantage, given the difficulties of obtaining perfectly matched visible-NIR images on operative scenarios. However, since it is a DL model, it needs to train on high amounts of visible-NIR image pairs, thus making its performance dependent on the quality of the training dataset. Finally, although the authors include several techniques to stabilize the training of the network, it is relevant to note that, in general, GANs have the open problem of training stability [74].  
Finally, in Figure 6, we show a sample of all the image types employed in the present paper.  
Figure 5: Structure of the _Fire-GAN_ model proposed by [PERSON] et al. [34].