Faint Space Debris Detection Algorithm Based on Small Aperture Telescope Detection System

Abstract

Ground-based photoelectric observation has unique advantages in space target observation. However, due to the weak light-gathering ability of small-aperture optoelectronic observation telescopes, the space debris in the image is weak and easily drowned in noise. In order to solve the above problems, we use digital image processing technology to extract faint space debris. We propose a high detection rate space debris automatic extraction algorithm, aiming to automatically detect space debris. We first establish a new space target description model. It is mainly divided into two stages. The purpose of the first stage is to reduce the influence of a large number of stars. A hybrid filtering of wavelet transform and guided filtering for three consecutive frames, and the reconstructed wavelet is taken the median value can achieve the effect of eliminating stars. In the second stage, we adopt the method of robust principal component analysis and attribute the problem of target detection to the problem of separating the target and background of a single frame of image. After a large number of experimental results analysis, it is proved that the algorithm can effectively detect weak debris in the monitoring system of small aperture telescope, and has high precision and low computational complexity.

Dim and Small Space-Target Detection and Centroid Positioning Based on Motion Feature Learning

“…Liu et al [19] first use the maximum projection method and calculate the median image to remove stars; then, they use a target detection method based on projection time information to detect moving targets; and finally, they use inter-frame trajectory correlation to obtain the complete trajectory of the target. A two-stage detection algorithm was proposed by Jiang et al [20]. The first stage uses wavelet transform and guided filtering to eliminate the effect of stars, and the second stage uses a robust principal component analysis approach to attribute the target detection problem to the separation of the target and background in a single frame image, which can effectively detect the target.…”