# Deep Learning Methods for Space Situational Awareness
## 5 Conclusions and Future Directions  
Space infrastructures are subject to collisions with debris, abandoned space objects, and other active satellites every day. This is a big problem as modern society relies heavily on space infrastructure for day-to-day operations, such as communication, guidance, navigation, weather forecasting, and spatial images. Therefore, being aware of the spatial situation and developing further new algorithms for the defense of space infrastructures is very important. This review paper focused on such dramatic issues, reviewing the recent papers published in the field of machine and deep learning for object detection in satellite-based Internet of Things (SIoT) networks. In particular, the first part of our paper discussed the importance of SIoT, explaining what it is, why it is important, and its main uses in everyday life. Then, we have illustrated the problem related to space debris and the reasons why the protection of satellites' constellations is fundamental and necessary. The main methods of object detection were shown first without the use of deep learning frameworks and then with the use of CNN, which proved to be the best network to tackle this type of problem. In addition, innovative approaches were shown with other typologies of networks, discussing the use of Deep Reinforcement Learning. Finally, we conducted a comparison between several DL techniques for object detection in SSA scenarios via simulation results. Four different DL frameworks, namely Squeeze Net, Google Net, VGG-16, and Alex Net, were used for object detection for SSA applications. In particular, we simulated the use of a monostatic pulse radar that detects the radial velocity of moving \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Squeeze Net** & **VGG-16** & **Google Net** & **Alex Net** \\ \hline Precision & 0.937 & 0.875 & 0.852 & 0.925 \\ \hline Recall & 0.94 & 0.885 & 0.855 & 0.932 \\ \hline F-measure & 0.937 & 0.872 & 0.842 & 0.928 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision, Recall, and F-measure of DL Networks for SSA space debris detection. Figure 9: Overall accuracy of DL Networks in object detection for SSA applications. targets at specific intervals. The results obtained through simulations demonstrate the efficiency of such methods for object detection in SSA scenarios, thus improving the SIoT network resilience to collisions and damages with space debris. Radar and optical systems are both used in space surveillance. However, their performance and scope are very different. On the one hand, optical telescopes can observe objects at a great distance if the conditions are suitable and the angular velocities are low, but their efficiency highly depends on good weather conditions and adequate lighting during the night hours. On the other hand, radars are capable of tracking objects with higher angular velocities; they are available 24/7 and without restrictions of weather conditions. However, their sensitivity depends on the distance; radars are not very effective at tracking objects in higher positions compared to MEO (Medium Earth Orbit). Deep learning methods have been fully applied in radar detection. Using DL can not only process large amounts of data but also minimize or eliminate redundant and unimportant data. In addition, satellite imagery is ubiquitous in space, and networks such as CNN are especially good for working with imagery. Thus, characteristics that may not be identifiable to a human can be efficiently extracted by DL approaches. Furthermore, these approaches surpass traditional techniques in terms of accuracy. Not only DL models, such as radars, are robust to any meteorological change, but they also can work on any data, be it structured, unstructured, or semi-structured. The fusion of measurements from optical telescopes and the radar united with DL methodologies should provide highly acceptable orbit determination results because it is possible to combine the respective advantages and compensate for the respective disadvantages.