Effect Analysis of Optical Masking Algorithm for GEO Space Debris Detection

Abstract

A method with high detection rate, low false-alarm rate, and low computational cost is presented for removing stars and noise and detecting space debris with signal-to-noise ratio (SNR>3) in consecutive raw frames. The top-hat transformation is implemented firstly to remove background, then a masking technique is proposed to remove stars, and finally, a weighted algorithm is used to detect the pieces of space debris. The simulation samples are images taken by 600 mm ground-based telescope. And a series of simulation targets are added to the image in order to test the detection rate and false-alarm rate of different SNRs. The telescope in this paper is worked in “staring target mode.” The experimental results show that the proposed method can detect space debris effectively with low false-alarm by only three frames. When the SNR is higher than 3, the detection probability can reach 94%, and the false-alarm rate is below 2%. The running time of this algorithm is within 1 second. Additionally, algorithm performance tests in different regions are also carried out. A large set of image sequences are tested, which proves the stableness and effectiveness of the proposed method.

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

“…Finally, the proposed method is compared with the MHT method for streak-like dim and small object detection [51] with SN R = 6, 3, 2, and 1.5. We also compared this method with the EAOM algorithm with SN R = 6, 3, [25]. The result is shown in Table 5, and it is observed that the proposed method has a better detection performance than that of the MHT and EAOM methods.…”

“…Yang et al [24] proposed an integration algorithm for the detection of weak space debris using incoherent scatter radar. Kong et al [25] proposed an optical masking algorithm for GEO space debris detection. Its detection probability reaches 94% and false-alarm rate is below 2% when the SNR is higher than 3.…”

“…They demonstrated that this method is better than the Hough transform and phase congruency transform. Kong et al [25] proposed an effect analysis of the optical masking (EAOM) algorithm, which can detect GEO space debris with a low SNR based on a top-hat transformation, masking technique, and weighted algorithm. Jia et al [42] built an astronomical object detection and classification pipeline based on the Faster R-CNN, ResNet-50 [43] and Feature Pyramid Network [44].…”

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

“…Many space debris detection methods have been designed in recent years. Kong et al 1 used top-hat and a masking method to remove most of stars in the scene, which was realized by using a ground-based telescope. Xi et al 2 used feature learning of candidate regions by a trained deep learning network to detect space debris, and one dimensional mean iteration method was proposed to correct the nonuniform background information.…”

An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris

“…GSO debris extraction algorithms can be divided into feature extraction based on shape difference and target enhancement through multi-frame cumulative energy. The main methods are masking method [23], continuous frame image comparison method [24], point spread function fitting method [25], mathematical morphology method [26] and so on. Generally, feature extraction algorithms have high complexity and require prior feature information of target or background; multi-frame energy accumulation algorithm needs to ensure efficient suppression of stars and background.…”