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Journal ArticleDOI

Space debris detection in optical image sequences.

TLDR
A high-accuracy, low false-alarm rate, and low computational-cost methodology for removing stars and noise and detecting space debris with low signal-to-noise ratio (SNR) in optical image sequences is presented.
Abstract
We present a high-accuracy, low false-alarm rate, and low computational-cost methodology for removing stars and noise and detecting space debris with low signal-to-noise ratio (SNR) in optical image sequences. First, time-index filtering and bright star intensity enhancement are implemented to remove stars and noise effectively. Then, a multistage quasi-hypothesis-testing method is proposed to detect the pieces of space debris with continuous and discontinuous trajectories. For this purpose, a time-index image is defined and generated. Experimental results show that the proposed method can detect space debris effectively without any false alarms. When the SNR is higher than or equal to 1.5, the detection probability can reach 100%, and when the SNR is as low as 1.3, 1.2, and 1, it can still achieve 99%, 97%, and 85% detection probabilities, respectively. Additionally, two large sets of image sequences are tested to show that the proposed method performs stably and effectively.

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Citations
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Journal ArticleDOI

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

TL;DR: The proposed feature learning of candidate regions (FLCR) method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.
Journal ArticleDOI

Space Target Detection in Complicated Situations for Wide-Field Surveillance

TL;DR: The spatiotemporal pipeline filtering can effectively remove the streak images of background stars and obtain candidate points and pruned in the tree structure combined with these candidate trajectories by using velocity and direction feature of moving objects.
Journal ArticleDOI

Space target detection in optical image sequences for wide-field surveillance

TL;DR: The experimental results show that this high precision and low computational-cost space target detection method can effectively detect faint space targets in wide-field surveillance under a long exposure time.
Journal ArticleDOI

Stray Light Elimination Method Based on Recursion Multi-Scale Gray-Scale Morphology for Wide-Field Surveillance

TL;DR: Zhang et al. as mentioned in this paper proposed an accurate stray light elimination method based on recursion multi-scale gray-scale morphology (RMGM), which can make full use of the difference information between the target region and the surrounding background region.
Journal ArticleDOI

Effect Analysis of Optical Masking Algorithm for GEO Space Debris Detection

TL;DR: 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.
References
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TL;DR: An autonomous star tracker is an avionics instrument used to provide the absolute 3-axis attitude of a spacecraft utilizing star observations as mentioned in this paper, which consists of an electronic camera and associated processing electronics.
Proceedings ArticleDOI

Max-mean and max-median filters for detection of small targets

TL;DR: In this paper, the authors investigated the usefulness of Max-Mean and Max-Median filters in preserving the edges of clouds and structural backgrounds, which helps in detecting small-targets.
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Dynamic Programming Solution for Detecting Dim Moving Targets

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Journal ArticleDOI

Analysis of new top-hat transformation and the application for infrared dim small target detection

TL;DR: Good performance of the application for infrared dim small target detection is obtained, which could be ascribed to the proper selection of structuring elements based on the properties and three types of multi-scale operations are discussed in detail.
Journal ArticleDOI

Detecting small, moving objects in image sequences using sequential hypothesis testing

TL;DR: An algorithm is proposed for the solution of the class of multidimensional detection problems concerning the detection of small, barely discernible, moving objects of unknown position and velocity in a sequence of digital images, modeled as GWN.
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