# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 1 Introduction
The representative TBD-based space debris detection methods include the stacking method [16; 17], line-identifying technique [18], particle filter (PF) [19; 20], and multistage hypothesis testing (MHT)-based methods [21; 22; 23]. However, there are many limitations to these methods. The stacking method needs to presume a number of likely movement paths and requires time to analyze the observation data for the uncataloged space debris. The line-identifying technique assumes the space debris moving with a constant velocity and the analysis time will rise exponentially with the number of candidate detections in each frame. PF-based methods need to carefully design the likelihood function to confirm the trade-off between particle convergence and final tracks under the computational restrictions. For MHT-based methods, candidate trajectories will increase rapidly with the number of space objects and noise, resulting in high computational costs. The optical flow-based methods [24; 25; 26] can be performed without prior information but they are also limited by their high computational complexity. With the progress in satellite imaging technology, video satellite image data have been introduced to detect space objects in recent years [27; 28]. Different from multi-frame image sequences taken with long exposure times and long interval times, video image data can capture spatial information as well as more compact temporal information with the continuous movement state of space objects, which is vital for the immediate orbital anomaly detection and continuous monitoring. Typical video satellites and their key parameters are listed in Table 1.  
[PERSON] et al. [28] proposed a space object detection algorithm based on motion information using the Tiantuo-2 video satellite. The author removed the image background based on local image properties for a single-frame first. The space object trajectories are associated with the Kalman filter for all frames. Finally, the trajectory is considered a space object if the mean velocity of the object in all frames is larger than a given threshold. The algorithm can detect moving space objects with brightness changes from satellite videos, but it is not a straightforward process. Moreover, many parameters should be set up through empiricism or experiment in advance. The advantages and disadvantages of the above three methods are summarized in Table 2.  
Over the past decade, deep learning has made great advancements in computer vision [33], and deep convolutional neural networks have shown significant competence for video salient object detection (VSOD) [34; 35; 36]. The central principle of the VSOD involves learning temporal dynamic cues related to moving objects. based on the fact that long-range dependencies exist in the space and time of consecutive frames [37; 38]. [PERSON] [39] proposed a non-locally enhanced temporal module to construct the spatiotemporal connection between the features of input video frames. [PERSON] [34] developed a nonlocal self-attention scheme to capture the global information in the video frame. The intra-frame contrastive loss helps separate the foreground and background features and inter-frame contrastive loss improves temporal consistency. [PERSON] [37] introduced the transformer block to capture the long-range dependencies among group-based images through the self-attention mechanism and designed an intra-MLP learning module to avoid partial activation to further enhance the network. However, using VSOD directly for space debris detection is questionable. The reasons can be concluded as follows: (1) The large distances involved in detecting space debris can make the images appear very small, and they are often referred to as \"small objects\". Therefore, it is difficult to extract spatial features of space debris in videos by deep neural networks. (2) The cluttered backgrounds generated by large numbers of stars appearing as point source objects can make it difficult to distinguish space debris from star backgrounds.