Ground-based optical 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. Our algorithm is mainly divided into two stages. The purpose of the first stage is to reduce the influence of a large number of stars. We perform wavelet transform and guided filtering for three consecutive frames, and the reconstructed wavelet that takes 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 faint debris in the monitoring system of small aperture telescope, and has high precision and low computational complexity.
instrumentation: detectors – methods: data analysis – methods: observational
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