# Deep Learning Methods for Space Situational Awareness
## 2 Related Works
### DL-Based Methods
#### 2.2.3 Hybrid Convolutional Prediction Models  
In [106], a new model for zero-click action recognition is studied, which jointly captures the object relations of a static frame and models the temporal motion patterns of adjacent frames. The object detector first detects and extracts the characteristics of the object. The convolutions of the graph are then conducted to effectively exploit object relationships. The work presented in [107] provides a way of planning the use of solar energy. Accurate production forecasts are essential for photovoltaic projects to achieve stable power. Traditional forecasts based on soil observation time series are largely troubled by the problem of phase shift due to incomplete consideration of the impacts of cloud movement. This work develops an innovative framework that integrates ground-based and satellite observations through DL to improve photovoltaic production forecasts. The framework is based on a hybrid deep-prediction model to account for the impact of cloud movements on subsequent changes in solar radiation, a geometric calculator to simulate the relative position between the Sun and photovoltaic panels, and a photovoltaic estimator to model the performance of photovoltaic modules during photoelectric conversions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Description** & **Reference Number** \\ \hline \multirow{8}{*}{Convolutional Neural Networks} & This paper shows that DL architectures are suitable for modeling complex behaviors of conventional multimodal datasets. It is demonstrated that DL models have the ability to work on any data, be it structured, unstructured or semi-structured & [96] \\ \cline{1-1}  & The authors propose a method for detecting the salience of space debris based on a fully convolutional network (FCN) for the space surveillance platform. & [97] \\ \cline{1-1}  & A novel U-Net deep neural network approach is exploited for image segmentation for real-time extraction of tracklets from optical acquisitions & [98] \\ \cline{1-1}  & The use of hierarchical MCNNT (Modified Convolutional Neural Networks Techniques) improves the performance of CNN classification, reaching a 96\% accuracy against existing support vector machine (SVM) models. & [99] \\ \cline{1-1}  & 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. & [100] \\ \cline{1-1}  & An end-to-end spacecraft image segmentation network using the DeepLabv3 + semantic segmentation network as the basic framework is proposed here. Then, a multiscale neural network based on sparse convolutions (called SISnet) is developed. & [101] \\ \cline{1-1}  & Conventional classification algorithms, such as k-nearest neighbor (k-NN), are implemented and compared in terms of accuracy with the proposed DNN-based classification algorithms, including the popular CNN and the recurrent neural network. & [31] \\ \hline \multirow{8}{*}{YOLOv3-based networks} & A simple two-step framework for recognizing moving objects is proposed. In the first step, regions of interest (ROIs) are found in a video frame. In the second step, a CNN is used to rank the detected ROIs based on a set of predefined criteria. & [103] \\ \cline{1-1}  & YOLOv3-Tiny optimizes the YOLOv3-based network structure and reduces the output by one scale. Human images are used to train YOLOv3-Tiny so that they can recognize humans as tracking targets. Then, YOLOv3-Tiny analyzes the images collected by the UAV for target tracking. & [105] \\ \cline{1-1}  &  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Description** & **Reference Number** \\ \hline \multirow{8}{*}{Convolutional Neural Networks} & This paper shows that DL architectures are suitable for modeling complex behaviors of conventional multimodal datasets.