Classification and Recognition of Space Debris and Its Pose Estimation Based on Deep Learning of CNNs

Afshar, Roya; Lu, Shiping

doi:10.1007/978-3-030-50726-8_79

Cited by 5 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Space object identification using ResNet18 [20] obtained an accuracy of 99.96%. Afshar and Lu [27] also implemented the same method for satellite classification and pose estimation achieving promising results. To further resolve limited data, they also used data augmentation to improve performance.…”

Section: Related Workmentioning

confidence: 99%

Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

et al. 2023

View full text Add to dashboard Cite

Space situational awareness (SSA) system requires recognition of space objects that are varied in sizes, shapes, and types. The space images are challenging because of several factors such as illumination and noise and thus make the recognition task complex. Image fusion is an important area in image processing for various applications including RGB-D sensor fusion, remote sensing, medical diagnostics, and infrared and visible image fusion. Recently, various image fusion algorithms have been developed and they showed a superior performance to explore more information that are not available in single images. In this paper, we compared various methods of RGB and Depth image fusion for space object classification task. The experiments were carried out, and the performance was evaluated using 13 fusion performance metrics. It was found that the guided filter context enhancement (GFCE) outperformed other image fusion methods in terms of average gradient (8.2593), spatial frequency (28.4114), and entropy (6.9486). additionally, due to its ability to balance between good performance and inference speed (11.41 second), GFCE was selected for RGB and Depth image fusion stage before feature extraction and classification stage. The outcome of fusion method is fused images that were used to train a deep ensemble of CoAtNets to classify space objects into ten categories. The deep ensemble learning methods including bagging, boosting, and stacking were trained and evaluated for classification purposes. It was found that combination of fusion and stacking was able to improve classification accuracy largely compared to the baseline methods by producing an average accuracy of 89 % and average F1 score of 89 %.

show abstract

Section: Related Workmentioning

confidence: 99%

Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The intuition here is that classical machine learning methods may allow us to effectively grapple with the astrodynamic complexity and multidimensionality of RSO classification. The main focus of research to date has been on improving the accuracy of orbital forecasting or improving the RSO classification value of sensor data [1,22,31,56]. To our knowledge, no attempt has been made to tackle the RSO task with a feature set as limited as the one available to defenders in our threat model.…”

Section: Why Machine Learning?mentioning

confidence: 99%

On Detecting Deception in Space Situational Awareness

Pavur

Martinovic

2021

Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security

View full text Add to dashboard Cite

Space Situational Awareness (SSA) data is critical to the safe piloting of satellites through an ever-growing field of orbital debris. However, measurement complexity means that most satellite operators cannot independently acquire SSA data and must rely on a handful of centralized repositories operated by major space powers. As interstate competition in orbit increases, so does the threat of attacks abusing these information-sharing relationships. This paper offers one of the first considerations of defense techniques against SSA deceptions. Building on historical precedent and real-world SSA data, we simulate an attack whereby an SSA operator seeks to disguise spy satellites as pieces of debris. We further develop and evaluate a machine-learning based anomaly detection tool which allows defenders to detect 90-98% of deception attempts with little to no in-house astrometry hardware.Beyond the direct contribution of this system, the paper takes a unique interdisciplinary approach, drawing connections between cyber-security, astrophysics, and international security studies. It presents the general case that systems security methods can tackle many novel and complex problems in an historically neglected domain and provides methods and techniques for doing so.

show abstract

“…(3) Machine learning methods. With the development of artificial intelligence technology, some scholars have applied artificial intelligence algorithms to the attitude estimation of space targets and achieved some research results [17,18]. However, it is difficult to establish a perfect electromagnetic simulation model for non-cooperative space targets, so it is hard to obtain the massive set of training samples required by machine learning algorithms, which leads to difficulties in obtaining good estimation performance and limits the wide application of artificial intelligence technology in the field of space target attitude estimation.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence

Li,

Chen,

Cao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The stripmap mode is a basic and important mode for spaceborne synthetic aperture radars (SARs). Estimating the time-varying area observed by spaceborne SARs operating in stripmap mode is a practical but challenging field of research. In this article, we propose a novel method that parameterizes the time-varying area observed by the spaceborne SAR operating in the boresight stripmap mode into a fixed antenna attitude. Based on the principle of minimizing the dihedral angle between the plane containing the ideal estimated scatterers and the plane containing the actual parabolic antenna edge of a spaceborne SAR, an objective function is established for estimating the area observed by a spaceborne SAR operating in the boresight stripmap mode. Then, simulation experiments are designed to validate the feasibility and the robustness of the proposed method. The experimental simulation results show that the proposed method is feasible, and even under low signal-to-noise ratio (SNR) conditions of 10 dB, the proposed method still has good robustness.

show abstract

Classification and Recognition of Space Debris and Its Pose Estimation Based on Deep Learning of CNNs

Cited by 5 publications

References 11 publications

Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

On Detecting Deception in Space Situational Awareness

Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence

Contact Info

Product

Resources

About