# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 1 Introduction  
With the advancements in space technology, the increasing number of operational spacecraft is facing a severe threat from space debris, which is caused by frequent space launch activities [1; 2]. According to the U.S. As of March 2022, the Space Surveillance Network (SSN) has cataloged up to 25,000 space debris, defunct spacecraft, and active spacecraft, with numbers expected to increase continuously in the future [3]. Collisions with large space debris could destroy an entire spacecraft, while small-sized space debris could cause irreversible damage to spacecraft due to their high velocities, such as performance degradation or dysfunction. Therefore, it is vital to observe space debris in the distance to avoid latent collisions with spacecraft. One crucial step involves space debris detection, which could be further used for space debris tracking and cataloging. There are currently two main categories of space debris surveillance systems: space-based systems and ground-based systems. Ground-based systems rely on large telescopes or radar installed on the ground for space debris detection and recognition. In contrast, space-based systems use onboard sensing devices to detect space debris. The advantages of space-based systems over ground-based systems can be described as follows. (1) Theyare not affected by weather or the circadian rhythm. (2) They could avoid the limits of stationary observation sites. (3) They could detect millimeter-sized small objects; the latter is aimed at centimeter-sized objects [4]. Therefore, space-based surveillance systems are effective measures for enhancing the safety levels of spacecraft. For the application of space debris surveillance, onboard sensing devices generally contain visible sensors, infrared sensors, and radars. Among the sensing devices, visible sensors are promising solutions due to their superior autonomous levels and highly accurate observation data [5]. The typical procedure for detecting space debris involves object extraction and centroid computation [6; 7]. The centroid can then be used for astrometry by matching the observed star field with the star catalog [8]. However, the main focus of this paper is object extraction; a simple centroid computational method was adopted to evaluate the proposed object extraction method. This paper does not cover the astrometry problem. Based on different operational modes of the surveillance system, commonly used visible sensor-based space debris detection strategies can be divided into streak-like object detection and point-like object detection. When the orientation of the surveillance platform is continuously fixed to stars, the space objects appear as streak-like regions; this operational mode is called sidereal tracking. When the surveillance platform is continuously reoriented to be fixed at the space objects, the objects appear as point-like regions; this mode is called object tracking [9]. Afterward, space debris and stars could be distinguished by their shape characteristics in both operation modes using only single-frame images. Typical single-frame image-based methods for space debris detection include the Hough transform method [10], feature-based methods using image moments [7], point spread function (PSF) fitting techniques [11; 12], mathematical morphological methods [13; 14], etc. However, faint space debris could not be detected by single-frame images due to the tiny observable magnitude limited by charge-coupled device (CCD) sensors [15]. To deal with this problem, multi-frame image-based methods have been proposed using the space object's motion information, which can be divided into two categories: track-before-detect (TBD) methods and optical flow methods. The main idea of TBD is to improve the signal-to-noise ratio (SNR) of faint space objects according to accumulating measurements to produce more confident detections.