Space Debris Automation Detection and Extraction Based on a Wide-field Surveillance System

Abstract

Wide-field telescopes with long exposure times have stronger space target detection capabilities. However, complex background sky conditions will still cause a series of difficulties in detecting space debris, such as a large number of star points, a large amount of noise, and the discontinuity and nonlinearity of the target. We propose a space debris automatic extraction channel with a high detection rate and low computational cost to solve these difficulties. We apply an improved median filter for noise elimination and then the double-structure morphological filter algorithm used to suppress the background of the star image to eliminate star points and noise. Then, the guided filter was used to eliminate residual noise, and star points were used to reduce the impact on the target. Finally, the improved Hough transform was also applied to detect the target in the image. Our automatic extraction algorithm is used in real astronomical star maps, including different orbiting satellites (star-tracking mode). These images were obtained by using a 280 mm diameter telescope, which was located in Changchun Observatory. The experimental results demonstrated the effectiveness of the extraction algorithm in this study. It can effectively detect and track space targets in a long-exposure wide-field surveillance system and has high positioning accuracy and low computational complexity, which solves the problem of space debris extraction under a complex background.

Weak Spatial Target Extraction Based on Small-Field Optical System

“…However, when processing multi-frame projection images, the median filtering algorithm is prone to destroying trajectories. Reference [ 12 ] proposes an improved median filtering algorithm, while reference [ 13 ] proposes a local threshold filtering method. These methods have improved compared to traditional median filtering in processing starry image, but there are still some shortcomings.…”

“…Reference [ 10 ] proposes a spatial object detection algorithm based on robust features, but it is not suitable for weak targets with only a few pixels. References [ 11 , 12 ] use guided filtering to remove stars and identify target motion fringes, but it is difficult to directly generate stable and continuous target fringes in high-orbit small-field spatial object detection. References [ 13 , 14 ] use deep learning algorithms to classify targets, but currently the model can only distinguish target motion stripes and point noise under different SNRs.…”

“…However, it is inaccurate and slow in multi-target detection. References [ 12 , 18 ] use Hough transform to detect target trajectories, but it requires detecting and removing stars in advance. References [ 19 , 20 ] use two-dimensional matched filtering to detect targets, but this method requires traversing paths in different directions, lengths, and shapes in images.…”

A Robust Space Target Extraction Algorithm Based on Standardized Correlation Space Construction

“…Compared with radar detection, optical detection has the following advantages: low cost, high accuracy, and low energy consumption. Based on optical observation data, the angular position of space debris can be measured, like R.A. and decl [5]. However, especially in the case of wide-field images showing multiple sources, the sizes and intensities of different objects present in the images can vary considerably [6].…”

A space debris detection and tracking method for wide-field surveillance

“…Liu et al 11 used a star sensor with multi-star space debris detecting and positioning on-board method to realize constant geocentric observation. Jiang et al 12 applied an improved median filter and a doublestructure morphological filter to eliminate noise and star points, and achieved the detection of targets using an improved Hough transform. Besides, Jiang et al 13 applied a guided filter to remove noise and the stars, achieved the detection and space debris tracking of targets using the Hough transform and the Kalman filter.…”