Claudio Tancredi
AI-Based On-Board Image Compression for Earth Observation Satellites.
Rel. Tatiana Tommasi, Francesco Rossi, Luca Romanelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
The growing demand for high-resolution imagery from Earth Observation (EO) satellites, driven by the need for better environmental monitoring, resource management, and disaster response, necessitates innovative approaches to on-board data compression to optimize downlink transmissions to ground stations. This thesis investigates the application of Artificial Intelligence (AI) techniques for on-board image compression. Unlike traditional algorithms, such as JPEG 2000, which have been the standard for many missions, AI-based models offer the potential for both greater compression efficiency and higher reconstruction quality. This research focuses on developing a lightweight Convolutional Autoencoder (CAE), a type of Convolutional Neural Network (CNN), for single-band image compression capable of operating within the limited resources available to satellite systems.
A baseline CAE is established and further optimized through various modifications to enhance its performance
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