Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation

• Published in: Applied sciences Vol. 13; no. 3; p. 1947
• Main Authors: Mastrofini, Marco, Agostinelli, Ivan, Curti, Fabio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Algorithms; Artificial intelligence; convolutional neural network; Methods; resident space objects; segmentation; Sensors; space situational awareness; star trackers
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13031947

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.