The present work focuses on the investigation of an artificial intelligence (AI) algorithm for brightest objects segmentation in night sky images’ field of view (FOV). This task is mandatory for many applications that want to focus on the brightest objects in an optical sensor image with a particular shape: point-like or streak. The algorithm is developed as a dedicated application for star sensors both for attitude determination (AD) and onboard space surveillance and tracking (SST) tasks. Indeed, in the former, the brightest objects of most concern are stars, while in the latter they are resident space objects (RSOs). Focusing attention on these shapes, an AI-based segmentation approach can be investigated. This will be carried out by designing, developing and testing a convolutional neural network (CNN)-based algorithm. In particular, a U-Net will be used to tackle this problem. A dataset for the design process of the algorithm, network training and tests is created using both real and simulated images. In the end, comparison with traditional segmentation algorithms will be performed, and results will be presented and discussed together with the proposal of an electro-optical payload for a small satellite for an in-orbit validation (IOV) mission.

Design and validation of a U-net-based algorithm for star sensor image segmentation / Mastrofini, Marco; Agostinelli, Ivan; Curti, Fabio. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 13:3(2023), pp. 1-27. [10.3390/app13031947]

Design and validation of a U-net-based algorithm for star sensor image segmentation

Marco Mastrofini
Conceptualization
;
Ivan Agostinelli
Validation
;
Fabio Curti
Methodology
2023

Abstract

The present work focuses on the investigation of an artificial intelligence (AI) algorithm for brightest objects segmentation in night sky images’ field of view (FOV). This task is mandatory for many applications that want to focus on the brightest objects in an optical sensor image with a particular shape: point-like or streak. The algorithm is developed as a dedicated application for star sensors both for attitude determination (AD) and onboard space surveillance and tracking (SST) tasks. Indeed, in the former, the brightest objects of most concern are stars, while in the latter they are resident space objects (RSOs). Focusing attention on these shapes, an AI-based segmentation approach can be investigated. This will be carried out by designing, developing and testing a convolutional neural network (CNN)-based algorithm. In particular, a U-Net will be used to tackle this problem. A dataset for the design process of the algorithm, network training and tests is created using both real and simulated images. In the end, comparison with traditional segmentation algorithms will be performed, and results will be presented and discussed together with the proposal of an electro-optical payload for a small satellite for an in-orbit validation (IOV) mission.
2023
Artificial intelligence; star trackers; resident space objects; convolutional neural network; segmentation; space situational awareness; space surveillance and tracking; optical images; in orbit validation
01 Pubblicazione su rivista::01a Articolo in rivista
Design and validation of a U-net-based algorithm for star sensor image segmentation / Mastrofini, Marco; Agostinelli, Ivan; Curti, Fabio. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 13:3(2023), pp. 1-27. [10.3390/app13031947]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1667858
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