Topological Sweep for Multi-Target Detection of Geostationary Space Objects

Liu, Daqi; Chen, Bin; Chin, Tat-Jun; Rutten, Mark

doi:10.1109/tsp.2020.3021232

Cited by 18 publications

(5 citation statements)

References 24 publications

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“…Previous efforts use a stacking or streak characterization approach for classification problems. The closest comparison is made to [ 30 ], which studies GEO object tracks over long exposure images.…”

Section: Evaluation Metrics and Analysismentioning

confidence: 99%

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RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor

Dave

Clark

Lee

2022

Sensors

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A catalogue of over 22,000 objects in Earth’s orbit is currently maintained, and that number is expected to double within the next decade. Novel data collection regimes are needed to scale our ability to detect, track, classify and characterize resident space objects in a crowded low Earth orbit. This research presents RSOnet, an image-processing framework for space domain awareness using star trackers. Star trackers are cost-effective, flight proven, and require basic image processing to be used as an attitude-determination sensor. RSOnet is designed to augment the capabilities of a star tracker by becoming an opportunistic space-surveillance sensor. Our research demonstrates that star trackers are a feasible source for RSO detections in LEO by demonstrating the performance of RSOnet on real detections from a star-tracker-like imager in space. RSOnet convolutional-neural-network model architecture, graph-based multi-object classifier and characterization results are described in this paper.

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Section: Evaluation Metrics and Analysismentioning

confidence: 99%

“…Compared to [ 30 ], RSOnet-derived MOT implementation achieves a lower classification accuracy and precision, but a comparable recall value. In the compared literature, GEO objects traverse the imager’s field of view at a slower speed than for RSOnet’s FAI images.…”

Section: Evaluation Metrics and Analysismentioning

confidence: 99%

RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor

Dave

Clark

Lee

2022

Sensors

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“…However, this method is limited by the requirement of a large aperture telescope that may not be easily accessible and may not be suitable for detecting smaller debris. Liu et al [12] proposed a geometric dual multi-object detection method that is robust to initialization, enabling simultaneous detection of multiple spatial objects. However, this method may not be suitable for complex environments with overlapping or closely spaced objects.…”

Section: Introductionmentioning

confidence: 99%

Finding a Needle in a Haystack: Faint and Small Space Object Detection in 16-Bit Astronomical Images Using a Deep Learning-Based Approach

Jiang,

Tang,

Ying

2023

Electronics

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With the increasing interest in space science exploration, the number of spacecraft in Earth’s orbit has been steadily increasing. To ensure the safety and operational integrity of active satellites, advanced surveillance and early warning of unknown space objects such as space debris are crucial. The traditional threshold-based filter for space object detection heavily relies on manual settings, leading to limitations such as poor flexibility, high false alarm rates, and weak target detection capability in low signal-to-noise ratios. Therefore, detecting faint and small objects against a complex starry background remains a formidable challenge. To address this challenge, we propose a novel, intelligent, and accurate detection method called You Only Look Once for Space Object Detection (SOD-YOLO). Our method includes the following novel modules: Multi-Channel Histogram Truncation (MHT) enhances feature representation, CD-ELAN based on Central Differential Convolution (CDC) facilitates learning contrast information, the Space-to-Depth (SPD) module replaces pooling layer to prevent small object feature loss, a simple and parameter-free attention module (SimAM) expands receptive field for Global Contextual Information, and Alpha-EIoU optimizes the loss function for efficient training. Experiments on our SSOD dataset show SOD-YOLO has the ability to detect objects with a minimum signal-to-noise ratio of 2.08, improves AP by 11.2% compared to YOLOv7, and enhances detection speed by 42.7%. Evaluation on the Spot the Geosynchronous Orbit Satellites (SpotGEO) dataset demonstrates SOD-YOLO’s comparable performance to state-of-the-art methods, affirming its generalization and precision.

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“…Yanagisawa et al [7] proposed a line-identifying technique combining a stacking method to detect uncatalogued GEO debris in CCD frames. Liu et al [8] introduced a multi-target detection algorithm for geostationary space objects based on topological sweep. They exploited the geometric duality to extract the target objects with an approximately linear trajectory.…”

Section: Introductionmentioning

confidence: 99%

Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects

et al. 2022

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The limited resource of Geostationary Earth Orbit (GEO) is precious and most telecommunication, weather and navigational satellites are placed in this orbit. In order to guarantee the safety and health of active satellites, advanced surveillance and warning of unknown space targets such as space debris are crucial. However, space object detection still remains a very challenging problem because of the weak target characteristics and complex star background. To solve this problem, we conduct a deep-learning-based framework called PP-YOLOv2 for single-frame object detection and design a post-processing algorithm named CFS for further candidate filtration and supplement. First, we transform the label information and generate the according bounding boxes to train the PP-YOLOv2 detector to extract candidate coordinates for each frame. Then, the CFS technique is designed as an effective post-processing procedure to obtain the eventual prediction results. Experiments were conducted over a dataset from the Kelvins SpotGEO challenge, which demonstrate the effectiveness and the comparable detection performance of our proposed pipeline. Finally, the deployment results on NVIDIA Jetson Nano show that the proposed method has a competitive application prospect for a space target monitoring system.

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Topological Sweep for Multi-Target Detection of Geostationary Space Objects

Cited by 18 publications

References 24 publications

RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor

RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor

Finding a Needle in a Haystack: Faint and Small Space Object Detection in 16-Bit Astronomical Images Using a Deep Learning-Based Approach

Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects

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