Abstract
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.
Abstract (translated)
本文讨论了从微型卫星中恢复超光谱图像（HSI）面临的挑战，这些卫星通常受到条带效应的影响，并且计算资源有限。我们提出了一个轻量级的实时压缩感知（RTCS）网络，旨在实现高效且在条带效应和噪声传输条件下具有鲁棒性的HSI重构。RTCS网络具有简化架构，减少了所需的训练样本，并使条带型HSI的压缩感知变得容易，与迷你火箭扫描机制上的中等设计完全吻合。这 contrasts 基于优化的模型，这些模型需要高精度的浮点运算，使得它们难以在边缘设备上部署。我们的编码器采用了一个兼容整数8的线性投影来传输条带型HSI数据，实现实时压缩感知。此外，根据新颖的双流架构，在接收端提出了一种高效的HSI恢复解码器，允许在没有复杂中央服务器的情况下实现边缘设备重建。随着越来越多的微型卫星需要地面站的大量计算资源，这种方法的重要性也越来越突出。大量实验验证了我们的方法的优越性能，为现有的微型卫星系统提供了新的和至关重要的功能。
URL
https://arxiv.org/abs/2404.15781
