Refined STACK-CNN for Meteor and Space Debris Detection in Highly Variable Backgrounds

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 10432 - 10453
• Main Authors: Olivi, Leonardo, Montanaro, Antonio, Bertaina, Mario Edoardo, Coretti, Antonio Giulio, Barghini, Dario, Battisti, Matteo, Belov, Alexander, Bianciotto, Marta, Bisconti, Francesca, Blaksley, Carl, Blin, Sylvie, Bolmgren, Karl, Cambie, Giorgio, Capel, Francesca, Casolino, Marco, Churilo, Igor, Crisconio, Marino, De La Taille, Christophe, Ebisuzaki, Toshikazu, Eser, Johannes, Fenu, Francesco, Filippatos, George, Franceschi, Massimo Alberto, Fuglesang, Christer, Golzio, Alessio, Gorodetzky, Philippe, Kajino, Fumiyoshi, Kasuga, Hiroshi, Klimov, Pavel, Kungel, Viktoria, Kuznetsov, Vladimir, Manfrin, Massimiliano, Marcelli, Laura, Mascetti, Gabriele, Marszal, Wlodzimierz, Mignone, Marco, Miyamoto, Hiroko, Murashov, Alexey, Napolitano, Tommaso, Ohmori, Hitoshi, Olinto, Angela, Parizot, Etienne, Picozza, Piergiorgio, Piotrowski, Lech Wiktor, Plebaniak, Zbigniew, Prevot, Guillaume, Reali, Enzo, Ricci, Marco, Romoli, Giulia, Sakaki, Naoto, Sharakin, Sergei, Shinozaki, Kenji, Szabelski, Jacek, Takizawa, Yoshiyuki, Vagelli, Valerio, Valentini, Giovanni, Vrabel, Michal, Wiencke, Lawrence, Zotov, Mikhail
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Detectors; Detritus; Earth; International Space Station; Machine learning; Meteors; Meteors & meteorites; Neural network applications; Neural networks; Remote sensing; Space debris; Space debris mitigation; space technology; Space vehicles; Telescopes
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3397734

In this article, we present cutting-edge machine learning-based techniques for the detection and reconstruction of meteors and space debris in the Mini-EUSO experiment, a detector installed on board of the International Space Station, and pointing toward the Earth. We base our approach on a recent technique, the STACKing method plus Convolutional Neural Network (STACK-CNN), originally developed as an online trigger in an orbiting remediation system to detect space debris. Our proposed method, the refined-STACKing method plus convolutional neural network (R-Stack-CNN), makes the STACKing method plus convolutional neural network (STACK-CNN) more robust, thanks to a random forest that learns the temporal development of these events in the camera. We prove the flexibility of our method by showing that it is sensitive to any space object that moves linearly in the field of view. First, we search small space debris, never observed by Mini-EUSO. Due to the limiting statistics, also in this case, no debris were found. However, since meteors produce signals similar to space debris but they are much more frequent, the R-Stack-CNN is adapted to identify such events while avoiding the numerous false positives of the Stack-CNN. Results from real data show that the R-Stack-CNN is able to find more meteors than a classical thresholding method and a new method of two neural networks. We also show that the method is also able to accurately reconstruct speed and direction of meteors with simulated data.