Improved detection of faint streaks based on a streak-like spatial filter

Vananti, Alessandro; Schild, Klaus; Schildknecht, Thomas

doi:10.1016/j.asr.2019.10.006

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Angular data quality will further improve with improved time synchronization of the image exposure times and velocity aberration correction. Improvements in the image evaluation algorithms should be able to suppress the majority of the false-negative detections to approach near unity detection efficiency, by implementing more sophisticated image processing algorithms like presented by Vananti et al [ 25 ]. A different weather station is recommended to eliminate false clear sky identifications and short image recording during cloudy conditions.…”

Section: Discussionmentioning

confidence: 99%

APPARILLO: a fully operational and autonomous staring system for LEO debris detection

Wagner

Clausen

2021

CEAS Space J

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For safe operation of active space crafts, the space debris population needs to be continuously scanned, to avoid collisions of active satellites with space debris. Especially the low Earth orbit (LEO) shows higher risks of collisions due to the highest density of orbital debris. Laser ranging stations can deliver highly accurate distance measurements of debris objects allowing precise orbit determination and more effective collision avoidance. However, a laser ranging station needs accurate a priori orbit information to track an orbital object. To detect and track unknown orbital objects in LEO, here, a passive optical staring system is developed for autonomous 24/7 operation. The system is weather-sealed and does not require any service to perform observations. To detect objects, a wide-angle imaging system with 10° field of view equipped with an astronomical CCD camera was designed and set up to continuously observe the sky for LEO objects. The system can monitor and process several passing objects simultaneously without limitations. It automatically starts an observation, processes the images and saves the 2D angular measurements of each object as equatorial coordinates in the TDM standard. This allows subsequent initial orbit determination and handover to a laser tracking system. During campaigns at twilight the system detected up to 36 objects per hour, with high detection efficiencies of LEO objects larger than 1 m3. It is shown that objects as small as 0.1 m3 can be detected and that the estimated precision of the measurements is about 0.05° or 7 × the pixel scale.

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Section: Discussionmentioning

confidence: 99%

APPARILLO: a fully operational and autonomous staring system for LEO debris detection

Wagner

Clausen

2021

CEAS Space J

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“…In streak-like object detection, matched filters are widely used to identify targets by detecting image regions with high responses to streak-like filters [31][32][33]. However, this method can produce numerous stripe templates with different directions and lengths, leading to long computation times and high computational costs.…”

Section: Model-driven Methodsmentioning

confidence: 99%

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

Su,

Niu,

et al. 2023

Remote Sensing

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The detection of dim and small space-targets is crucial in space situational awareness missions; however, low signal-to-noise ratio (SNR) targets and complex backgrounds pose significant challenges to such detection. This paper proposes a space-target detection framework comprising a space-target detection network and a k-means clustering target centroid positioning method. The space-target detection network performs a three-dimensional convolution of an input star image sequence to learn the motion features of the target, reduces the interference of noise using a soft thresholding module, and outputs the target detection result after positioning via the offsetting branch. The k-means centroid positioning method enables further high-precision subpixel-level centroid positioning of the detection network output. Experiments were conducted using simulated data containing various dim and small space-targets, multiple noises, and complex backgrounds; semi-real data with simulated space-targets added to the real star image; and fully real data. Experiments on the simulated data demonstrate the superior detection performance of the proposed method for multiple SNR conditions (particularly with very low false alarm rates), robustness regarding targets of varying numbers and speeds, and complex backgrounds (such as those containing stray light and slow motion). Experiments performed with semi-real and real data both demonstrate the excellent detection performance of the proposed method and its generalization capability.

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“…Dawson et al (2016) incorporated signal-matched filters into the maximum likelihood statistical formalism to identify ultra-faint streaks. Vananti et al (2020) presented an algorithm for identifying streak-like regions using image filters. Although these filtering techniques perform well in faint object detection, they are too slow.…”

Section: Introductionmentioning

confidence: 99%

Detecting Moving Objects in Photometric Images Using 3D Hough Transform

Zhang,

Hu,

et al. 2024

PASP

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In response to the exponential growth of space debris, an increasing number of observation devices are being used for the observation of moving objects, such as space debris and asteroids, which require further improvements in data-processing capabilities for the detection of moving objects. In this study, we propose a rapid detection algorithm designed for detecting moving objects, leveraging the power of the 3D Hough transform. By the simulated image experiments, our results show that the detection rate increases with the number of continuous images when fully extracting objects. Based on this foundation, the object detection rate is at least 87% regardless of the object number in the image sequence when detecting objects from at least six continuous images. In the observed image experiments, we used source-extractor to extract sources. The results show the method can successfully detect objects with signal-to-noise ratio higher than three from sidereal tracking images and can identify asteroids from asteroid tracking images while maintaining a detection speed that meets the requirements for real-time processing.

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Improved detection of faint streaks based on a streak-like spatial filter

Cited by 9 publications

References 5 publications

APPARILLO: a fully operational and autonomous staring system for LEO debris detection

APPARILLO: a fully operational and autonomous staring system for LEO debris detection

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

Detecting Moving Objects in Photometric Images Using 3D Hough Transform

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