# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 4 Results  
The SqueezeNet model achieved a good balance between training time and classification accuracy for automatically identifying volcanic thermal anomalies. The SqueezeNet  
Figure 3: Example of images for the two classes (**left**: “Yes Anomaly” ID 1, **right**: “No Anomaly” ID 0) fed to the SqueezeNet model. All the images were downloaded from Google Earth Engine.  
network, already trained with the ImageNet dataset, and retrained for five epochs in the transfer learning process, is tested over the test dataset. Ten runs of the SqueezeNet model are used to design the ensemble classifier. For each image, the outcome of the ensemble SqueezeNet is assigned as the most frequent outcome among the trained model runs. Generally speaking, each run provides good performances, as Figure 4 shows, with accuracy values ranging from 91.7% (for one run over the total ten runs) to 100.0% (for two runs over the total ten runs) and an average accuracy of about 96.2% (two images misclassified), with a minimum of zero and a maximum of five misclassified images over the sixty total images for all the ten runs of the trained SqueezeNet. The final accuracy of the ensemble SqueezeNet is then computed taking the mode of each run outcome, resulting in an overall accuracy of 98.3%, with a unique image misclassified over the total. In addition, this final ensemble model reaches a Precision of 100.0%, a Recall of 95.7%, and a F1 score of 97.8%, indicating a high-fidelity model.  
It is worth noting that we have inserted in the class 1 dataset not only images with clear active flows, but also images that show a presence of erupting vents. Even though this type of image does not show an active flow in progress, the scenes do show volcanic activity, and these kinds of anomalies could evolve towards different eruptive phenomena, including lava overflows.  
In order to investigate the reasons behind single run misclassifications, image samples misclassified by three different fine-tuning representative runs are shown in Figure 5. The first run (R1) is shown in Figure 5a,b, with images labeled as class 1 and classified as belonging to class 0, i.e., FN samples. The second run (R2) is shown in Figure 5c,d, with images labeled as class 0, classified as belonging to class 1, i.e., FP images. Finally, the third run (R3) is shown in Figure 5e,f, with an image labeled as class 1, classified as belonging to class 0 (Figure 5e) and an image belonging to class 0, classified as belonging to class 1 (Figure 5f). These misclassifications are probably due to the heterogeneity of the intraclass scenes, i.e., different kinds of volcanic thermal anomalies and volcanic at rest scenes. Indeed, these are randomly selected as training images and this may produce unbalanced training datasets, i.e., for example more scenes of active vents than lava flows for class 1, or more clear images than cloudy in class 0, thus polarizing the SqueezeNet model performances. That is why an ensemble approach allows for the highest output to be achieved from a small dataset while keeping high generalization capabilities. By considering the outcomes  
Figure 4: Accuracies of the 10 runs of the trained SqueezeNet model.  
of the ensemble SqueezeNet, only the scene in Figure 5b,e remains misclassified, even though this is a borderline case because the mode results in a 50% probability to belong to one of the classes and a 50% probability to belong to the other one.