Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat
<|reference_start|>Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat: This paper addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a Real-Time Compressed Sensing (RTCS) network designed to be lightweight and require only relatively few training samples for efficient and robust HSI reconstruction in the presence of the stripe effect and under noisy transmission conditions. The RTCS network features a simplified architecture that reduces the required training samples and allows for easy implementation on integer-8-based encoders, facilitating rapid compressed sensing for stripe-like HSI, which exactly matches the moderate design of miniaturized satellites on push broom scanning mechanism. This contrasts optimization-based models that demand high-precision floating-point operations, making them difficult to deploy on edge devices. Our encoder employs an integer-8-compatible linear projection for stripe-like HSI data transmission, ensuring real-time compressed sensing. Furthermore, based on the novel two-streamed architecture, an efficient HSI restoration decoder is proposed for the receiver side, allowing for edge-device reconstruction without needing a sophisticated central server. This is particularly crucial as an increasing number of miniaturized satellites necessitates significant computing resources on the ground station. Extensive experiments validate the superior performance of our approach, offering new and vital capabilities for existing miniaturized satellite systems.<|reference_end|>
@article{hsu2024real-time,
    title={Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission
  and Restoration for CubeSat},
    author={Chih-Chung Hsu, Chih-Yu Jian, Eng-Shen Tu, Chia-Ming Lee, Guan-Lin
  Chen},
    journal={arXiv preprint arXiv:2404.15781},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15781},
    primaryClass={cs.CV cs.AI eess.IV}
}