# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 5 Discussion
Figure 6 for this case presents bars for the statistical threshold algorithm and the X-Means model, showing that these two models had detected anomalies. In the same way, the 25 May 2021 case study presents a thermal anomaly at Mt. Etna, and this was detected only by fixed and statistical threshold algorithms and SqueezeNet, and not by X-Means (RF is not considered because a Landsat 8 image is used in this case and RF is trained only over Sentinel-2 data). Cases such as these, misclassified by the threshold and ML models and well classified by the DL model, highlight one of the strengths of the DL approaches, i.e., the capability to exploit not only the spectral features but also the spatial ones of the scenes under analysis. In detail, the presence of specific geometric shapes for the thermal anomalies or the background emphasizes the SqueezeNet classification capability; in contrast, the other methods do not consider the spatial features but only the spectral ones, linked to the spectral response of each pixel captured by the satellites [6]. Overall,  
Figure 6: Comparison between the detection capability of algorithms with fixed (light green bars) and statistical (dark green bars) thresholds, unsupervised (X-Means, orange bars) and supervised (RF, yellow bars) machine learning techniques and the SqueezeNet deep learning model (blue bars), with respect to the Ground Truth (dark red bars). Underlined data are related to Landsat 8 images used for those cases; otherwise, Sentinel-2 images are used. the SqueezeNet model gives the correct output for each of the test cases, except for the 25 February 2021 case at Pacaya volcano, in which thermal anomalies were present, but were not identified by the model. Figure 7 shows the accuracy (see Equation (1)) of the different methods for the detection of volcano thermal anomalies with respect to the ensemble SqueezeNet, by using Sentinel-2 and Landsat 8 data (accuracy percentages here rounded to the unit for ease of analysis). It is worth noting that a comparison of the accuracy with the RF model was possible only with the Sentinel-2 data because RF was not trained over the Landsat 8 data. Figure 7a is related to the comparison only for the Sentinel-2 data. The SqueezeNet (blue bar) has an accuracy of 100%. The fixed threshold method (light green bar) has a high accuracy of 98%. Instead, the statistical threshold method (dark green bar) has a lower accuracy of 74% and the X-Means (orange bar) has an accuracy of 60%. The RF model (yellow bar), in this case, has the same accuracy of the SqueezeNet one. However, the proposed model is readily available for S2 and L8 data, allowing for the detection of thermal anomalies reducing the processing dead times, taking advantage of the first available acquisition between the S2-MSI and L8-OLI. Figure 7b is related to the comparison only with the Landsat 8 data. The threshold-based algorithms have a lower accuracy than the precedent Sentinel-2 data case (Figure 7a), reaching 94% of accuracy for the fixed threshold model and 71% for the statistical threshold model, compared to the precedent 98% and 74%. In contrast, the X-Means accuracy is in growth (65% with respect to 60%). The SqueezeNet model reaches an accuracy of 94% (only one Landsat 8 misclassified image over the total). A similar accuracy is reached by a different model, using only one type of sensor. In contrast, SqueezeNet can be applied to any of the available satellite radiometric scenes captured by the different satellite sensors. Figure 7c is related to the comparison of both Sentinel-2 and Landsat 8 data considered together. The threshold-based algorithms have a slightly lower accuracy than the Sentinel-2 data case (Figure 7a), reaching 97% accuracy for the fixed threshold method and 73% for the statistical threshold method. Nevertheless, these values are higher than the second case with only Landsat 8 data (Figure 7b). In contrast, the X-Means model has 62% accuracy, showing an opposite trend, with higher accuracy than the first case and lower than the second one.