Earth observation with small satellites serves a wide range of relevant applications. However, significant advances in sensor technology (e.g., higher resolution, multiple spectrums beyond visible light) in combination with challenging channel characteristics lead to a communication bottleneck when transmitting the collected data to Earth. Recently, joint source coding, channel coding, and modulation based on neuronal networks has been proposed to combine image compression and communication. Though this approach achieves promising results when applied to standard terrestrial channel models, it remains an open question whether it is suitable for the more complicated and quickly varying satellite communication channel. In this paper, we consider a detailed satellite channel model accounting for different shadowing conditions and train an encoder-decoder architecture with realistic Sentinel-2 satellite imagery. In addition, to reduce the overhead associated with applying multiple neural networks for various channel states, we leverage attention modules and train a single adaptable neural network that covers a wide range of different channel conditions. Our evaluation results show that the proposed approach achieves similar performance when compared to less space-efficient schemes that utilize separate neuronal networks for differing channel conditions.

翻译：小型卫星地球观测服务于广泛的相关应用。然而，传感器技术（例如更高分辨率、可见光以外的多光谱）的显著进步，结合具有挑战性的信道特性，导致在将收集的数据传输到地球时出现通信瓶颈。最近，基于神经网络的联合信源编码、信道编码和调制被提出，以结合图像压缩与通信。虽然该方法在应用于标准地面信道模型时取得了有希望的结果，但它是否适用于更复杂且快速变化的卫星通信信道仍是一个悬而未决的问题。在本文中，我们考虑了一个详细的卫星信道模型，该模型考虑了不同的阴影条件，并使用真实的Sentinel-2卫星图像训练了一个编码器-解码器架构。此外，为了减少为不同信道状态应用多个神经网络所带来的开销，我们利用注意力模块并训练了一个单一的可适应神经网络，该网络覆盖了广泛的不同信道条件。我们的评估结果表明，与使用单独神经网络处理不同信道条件的空间效率较低的方案相比，所提出的方法实现了相似的性能。