Mr. Benjamin Haser Profile

Benjamin Haser

at Univ der Bundeswehr Munchen

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Proceedings Article | 13 June 2023 Presentation + Paper

Data pipeline of a multi-spectral satellite experiment for object detection and artificial intelligence-based processing

Markus Müller, Sanjay Swami, Benjamin Haser, Mohd Bilal, Artur Kinzel, Roger Förstner, Christian Mundt

Proceedings Volume 12521, 125210N (2023) https://doi.org/10.1117/12.2663468

KEYWORDS: Object detection, Image processing, RGB color model, Satellites, Clouds, Data modeling, Infrared imaging, Education and training, Artificial intelligence, Earth observing sensors

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Recent developments in the military domain introduce the need to detect and track hypersonic glide vehicles in Earth’s atmosphere. The Multispectral Object Sensing by Artificial Intelligence-processed Cameras (MOSAIC) experiment is part of the small-satellite ATHENE-1 of the Universit¨at der Bundeswehr M¨unchen. The primary scientific objective of MOSAIC is to demonstrate reliable detection, identification and tracking of hypersonic glide vehicles using primarily a cooled infrared camera and complementary a visual camera. To cope with a large amount of data from both high-resolution cameras in real-time, state-of-the-art computer vision on-board processing methods are used for detection and tracking. The secondary scientific objective is to investigate the efficiency and reliability of Artificial Intelligence (AI) based image processing algorithms and data compression for space applications. This is of particular importance given the high volumes and rates of data. The application of such algorithms requires a reliable and resource-efficient On-Board Computer (OBC) that can withstand the harsh space environment. The approach outlined in this paper envisions a dedicated OBC to manage the AI-based experiments of the satellite, called the Artificial Intelligence capable On-Board Computer (AI-OBC). The AI-OBC includes multiple hardware-based AI accelerators to meet the computational requirements and ensure real-time processing for object detection and tracking. This paper describes the structure of the data processing pipeline and includes the AI-OBC architecture with its connections to both the cameras and the platform’s OBC. Further, the study discusses the training and validation steps of the intended use-cases.

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