# Deep Learning Methods for Space Situational Awareness
## 2 Related Works
### DL-Based Methods
#### 2.2.1 CNN-Based Methods  
Convolutional neural networks have proven their worth in the areas of image recognition and classification. They are classified as deep learning (DL) algorithms, which take digital signals such as audio, images, and videos as inputs. They reached outstanding results in the field of image classification. Images go through discrete convolutional layers aiming to extract different aspects or features of the image and to assign them weights and biases to classify them. Different types of solutions have been implemented for tracking and classifying objects. In [97], the authors propose a method for detecting the salience of space debris based on a fully convolutional network (FCN) for the space surveillance platform. At the same time, the network directly learns the internal relationship between two frames, thus avoiding expensive optical flow calculations. However, the method is unsuitable for detecting small targets due to the limited features extracted from small space debris. In [98], a novel U-Net deep neural network approach is exploited for image segmentation for real-time extraction of tracklets from optical acquisitions. Artificially tagged images and synthetic footage of the night sky of objects passing over a specified location were artificially generated, and the corresponding labels were produced--stars appear as points in the field of view (FoV), while debris appears as streaks (usually called \"tracklet\"), resulting in a bright streak of pixels on the image. After a pre-processing step, the actual images were optimized for vignetting reduction and background brightness uniformity. In object recognition and classification, convolutional neural networks outperform not only \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Description** & **Reference Number** \\ \hline \begin{tabular}{c} Multi-static system for \\ the SSA mission. \\ \end{tabular} & \begin{tabular}{c} The main objective of this article is to evaluate the performance of a \\ multi-static system for the SSA mission. In addition, a simulation tool was \\ developed to test the performance in different scenarios. \\ \end{tabular} & [91] \\ \hline \
\begin{tabular}{c} Event camera \\ Object tracking \\ \end{tabular} &
\begin{tabular}{c} This article first introduces the basic principles of the event camera, then \\ analyzes its advantages and disadvantages. \\ \end{tabular} & [92] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._any of the techniques mentioned above but also any ML technique, such as demonstrated in the work [99]. Here, the authors obtained the best accuracy of the detected image objects by using intelligent surveillance cameras. The use of hierarchical MCNNT (Modified Convolutional Neural Networks Techniques) models of data processing modification improves the performance of CNN classification, reaching 96% of accuracy against existing support vector machine (SVM) models. A new type of convolutional network is presented in [100]. PSnet is a perspective-sensitive network to detect objects from different perspectives (i.e., angles of view). The features are mapped to the preset multi-perspective spaces to obtain the specific semantic feature of the object decoupled from the angle of view. With the help of PSnet, the overall separability of functionality is improved as objects between classes are projected onto different segments, and objects between classes with different viewing angles are projected onto the same segments. The work [101] proposes an end-to-end spacecraft image segmentation network using the DeepLabv3+ semantic segmentation network as the basic framework. A multiscale neural network based on sparse convolutions (SISnet) is developed. The innovation of this method is twofold--firstly, the ability to extract features is better from the dilated convolutional network, and secondly, the channel attention mechanism is introduced into the network to recalibrate the functionality responses.