Real-Time Plume Detection and Segmentation Using Neural Networks
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Real-Time Plume Detection and Segmentation Using Neural Networks Dwight Temple 1 Accepted: 29 September 2020/ # American Astronautical Society 2020
Abstract Applications of artificial intelligence have been gaining extraordinary traction in recent years across innumerable domains. These novel approaches and technological leaps permit leveraging profound quantities of data in a manner from which to elucidate and ease the modeling of arduous physical phenomena. ExoAnalytic collects over 500,000 resident space object images nightly with an arsenal of over 300 autonomous sensors; extending the autonomy of collection to data curation, anomaly detection, and notification is of paramount importance if elusive events are desired to be captured and classified. Efforts begin with rigorous image annotation of observed glints, streaking stars, and resident space objects with plumes from debris shedding events. Preliminary results permitted the successful classification of observed debris generating events from AMC-9, Telkom-1, and Intelsat-29e. After initial proof-of-concept, these events are incorporated into the training pipeline in order to characterize potentially unknown debris generating or anomalous events in future observations. The inclusion of a visual tracking system aides in reducing false alarms by roughly 30%. Future efforts include applications on both historical datamining as well as real-time indications and warnings for satellite analysts in their daily operations while maintaining a low probability of false alarm through detection and tracking algorithm refinement. Keywords Plume . Anomaly . Convolutional . Tracking . Satellite
Introduction An abundance of papers, proposals, and presentations regarding deep learning have inundated the recent literature of numerous conferences. Typically, these papers introduce novel methods to simplify arduous tasks with no closed-form mathematical solution. Convolutional, Temporal Convolutional and Recurrent Neural Networks * Dwight Temple [email protected]
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ExoAnalytic Solutions, Foothill Ranch, CA, USA
The Journal of the Astronautical Sciences
(CNNs, TCNs and RNNs)[1], Generative Adversarial Networks (GANs)[2], Reinforcement Learning (RL)[3], and attention-based decoder algorithms have been developed to solve tasks from image classification and segmentation, maneuvering target tracking, language translation and prediction, speech synthesis and emulation, as well as robotic action emulation. In the realm of Space Situational Awareness (SSA), previous work has been completed for sensor tasking using RL [4], maneuver detection using basic CNNs [5] and anomaly emulation and detection with static-pattern GANs and CNNs [6]. ExoAnalytic Solutions collects over 500,000 resident space object images nightly using their arsenal of over 300 ground based autonomous telescopes. With this enormous onslaught of incoming imagery and information, dissemination and discrimination are of paramount importance to prevent operator information overload. Creating a self-perpetuating an
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