The next generation of Earth observation satellites will seek to deploy intelligent models directly onboard the payload in order to minimize the latency incurred by the transmission and processing chain of the ground segment, for time-critical applications. Designing neural architectures for onboard execution, particularly for satellite-based hyperspectral imagers, poses novel challenges due to the unique constraints of this environment and imaging system that are largely unexplored by the traditional computer vision literature. In this paper, we show that this setting requires addressing three competing objectives, namely high-quality inference with low complexity, dynamic power scalability and fault tolerance. We focus on the problem of hyperspectral image denoising, which is a critical task to enable effective downstream inference, and highlights the constraints of the onboard processing scenario. We propose a neural network design that addresses the three aforementioned objectives with several novel contributions. In particular, we propose a mixture of denoisers that can be resilient to radiation-induced faults as well as allowing for time-varying power scaling. Moreover, each denoiser employs an innovative architecture where an image is processed line-by-line in a causal way, with a memory of past lines, in order to match the acquisition process of pushbroom hyperspectral sensors and greatly limit memory requirements. We show that the proposed architecture can run in real-time, i.e., process one line in the time it takes to acquire the next one, on low-power hardware and provide competitive denoising quality with respect to significantly more complex state-of-the-art models. We also show that the power scalability and fault tolerance objectives provide a design space with multiple tradeoffs between those properties and denoising quality.

翻译：新一代地球观测卫星将寻求在载荷上直接部署智能模型，以最大限度减少地面段传输与处理链路对时间敏感应用造成的延迟。为在轨执行设计神经网络架构，特别是针对星载高光谱成像仪，由于该环境与成像系统特有的约束条件（传统计算机视觉文献对此鲜有探讨）而面临全新挑战。本文论证该场景需同时满足三个相互制约的目标：低复杂度下的高质量推理、动态功耗可扩展性及容错能力。我们聚焦高光谱图像去噪问题——该任务对实现有效下游推理至关重要，并凸显了在轨处理场景的约束条件。我们提出一种神经网络设计，通过多项创新贡献解决上述三个目标：首先提出一种混合去噪器架构，既能抵御辐射引发的故障，又支持时变功耗调节；其次，每个去噪器采用创新架构，以因果方式逐行处理图像并保留历史行记忆，从而匹配推扫式高光谱传感器的数据采集过程，极大降低内存需求。实验表明，所提架构能在低功耗硬件上实现实时处理（即在采集下一行数据的时间内完成当前行处理），其去噪质量与显著更复杂的先进模型相比具有竞争力。研究还表明，功耗可扩展性与容错目标构成了多重特性权衡的设计空间，可在这些属性与去噪质量之间取得不同平衡。