Real-time on-board satellite cloud cover detection hardware architecture using spaceborne remote sensing imagery

Paola Vitolo; Andrea Fasolino; Rosalba Liguori; Luigi Di Benedetto; Alfredo Rubino; Gian Domenico Licciardo

doi:10.1117/12.3017554

20 June 2024 Real-time on-board satellite cloud cover detection hardware architecture using spaceborne remote sensing imagery

Paola Vitolo, Andrea Fasolino, Rosalba Liguori, Luigi Di Benedetto, Alfredo Rubino, Gian Domenico Licciardo

Author Affiliations +

Proceedings Volume 13000, Real-time Processing of Image, Depth, and Video Information 2024; 130000J (2024) https://doi.org/10.1117/12.3017554
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

To address these issues, this paper introduces a real-time on-board satellite cloud cover detection system based on a lightweight neural network. By discarding excessively cloudy images, the proposed approach can lead to an improvement in the efficiency and accuracy of satellite image-based systems. At the same time, it allows to minimize the data to be transmitted to the ground, consequently mitigating bandwidth problems and reducing transmission power. The proposed CNN shows a compact architecture, requiring fewer than 9 thousand parameters, while maintaining a detection accuracy of 89% when evaluated using the Landsat 8 dataset. An optimized hardware accelerator is designed to meet the on-board nanosatellites constraints. Post-implementation simulations on a Xilinx Artix 7 FPGA demonstrate state-of-the-art results with a utilization of about 12 thousand and 7 thousand of mapped LUTs and FFs, respectively, with a power consumption of 116 mW.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Paola Vitolo, Andrea Fasolino, Rosalba Liguori, Luigi Di Benedetto, Alfredo Rubino, and Gian Domenico Licciardo "Real-time on-board satellite cloud cover detection hardware architecture using spaceborne remote sensing imagery", Proc. SPIE 13000, Real-time Processing of Image, Depth, and Video Information 2024, 130000J (20 June 2024); https://doi.org/10.1117/12.3017554

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