Convolutional variational autoencoders for secure lossy image compression in remote sensing

Alessandro Giuliano; S. Andrew Gadsden; Waleed Hilal; John Yawney

doi:10.1117/12.3013451

6 June 2024 Convolutional variational autoencoders for secure lossy image compression in remote sensing

Alessandro Giuliano, S. Andrew Gadsden, Waleed Hilal, John Yawney

Proceedings Volume 13062, Sensors and Systems for Space Applications XVII; 130620H (2024) https://doi.org/10.1117/12.3013451
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The volume of remote sensing data is experiencing rapid growth, primarily due to the plethora of space and air platforms equipped with an array of sensors. Due to limited hardware and battery constraints the data is transmitted back to Earth for processing. The large amounts of data along with security concerns call for new compression and encryption techniques capable of preserving reconstruction quality while minimizing the transmission cost of this data back to Earth. This study investigates image compression based on Convolutional Variational Autoencoders (CVAE), which are capable of substantially reducing the volume of transmitted data while guaranteeing secure lossy image reconstruction. CVAEs have been demonstrated to outperform conventional compression methods such as JPEG2000 by a substantial margin on compression benchmark datasets. The proposed model draws on the strength of the CVAE’s capability to abstract data into highly insightful latent spaces and combining it with the utilization of an entropy bottleneck is capable of finding an optimal balance between compressibility and reconstruction quality. The balance is reached by optimizing over a composite loss function that represents the rate-distortion curve.

Conference Presentation

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

Citation Download Citation

Alessandro Giuliano, S. Andrew Gadsden, Waleed Hilal, and John Yawney "Convolutional variational autoencoders for secure lossy image compression in remote sensing", Proc. SPIE 13062, Sensors and Systems for Space Applications XVII, 130620H (6 June 2024); https://doi.org/10.1117/12.3013451

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