# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 5 Discussion  
The detection of volcanic thermal anomalies is obviously possible with several methods, ranging from most traditional threshold-based algorithms to most innovative machine learning based techniques. Traditional algorithms are based on the setting of thresholds to cluster the data. These thresholds can be either fixed a priori, obtained with a preliminary study over a group of training data, or selected adopting a statistical approach, calculating mean and standard deviation for each image [57]. In the first case, the analysis can be conducted comparing each pixel of an image with the fixed thresholds, in the second case, with the mean  
Figure 5: Images misclassified by three different fine-tuning runs: R1 case (**a,b**), images labeled as class 1, classified as belonging to class 0; R2 case (**c,d**), images labeled as class 0, classified as belonging to class 1; R3 case (**e**), image labeled as class 1, classified as belonging to class 0 and (**f**), image belonging to class 0, classified as belonging to class 1. All the images were downloaded from Google Earth Engine and directly given as input to the model. and standard deviation of each entire image, using an interval of confidence centered in the mean and with a radius of n \(\cdot\) standard deviations, typically setting n = 2 or n = 3. ML methods are based on either supervised or unsupervised learning. Labeled datasets, containing sample data that have been tagged with a target parameter, are employed in supervised learning techniques to train machine learning algorithms. The accuracy of the algorithm in interpreting the training data is evaluated using the target. In unsupervised approaches, unlabeled data are fed to the algorithm, which tries to extract common features, schemes or patterns, taking into account only the data similarity. To make a comparison of the performances of the threshold-based algorithms and ML techniques with the SqueezeNet model, we run them over the test set used in this study (Table 2). In particular, we used two algorithms reliant on fixed and statistical threshold approaches (with n = 3) [58; 59], an unsupervised X-Means ML model [1], and a supervised Random Forest (RF) ML model [60]. This last model was already trained using Sentinel-2 data to get the areal coverage of fresh anomalies, so the comparison with the RF model was made using only the Sentinel-2 images contained in the test set under analysis. Table 2 shows the correspondence between the date of the events in the test set used and the relative volcanoes and satellites for each event, with the ground truth values. Figure 6 shows the ability to detect the volcanic anomalies for all methods analyzed. The algorithms with fixed and statistical thresholds, the unsupervised and supervised machine learning techniques, and the deep learning model are compared for all the different events in the test set under analysis (Table 2). For each day in the date axis, the presence of a bar of a specific color refers to the detection of the anomaly for that specific date by the relative model, the absence of the bar refers to the absence of thermal anomaly detected on that day. In particular, the dark red bars are the ground truth results (i.e., the real labels), highlighting the real presence of volcanic thermal anomalies on a specific day, between 2013 and 2022. The green bars are related to the threshold-based algorithms, with the light green bars for the fixed threshold method and the dark green bars for the statistical threshold method. The orange