用神经形态芯片在轨实现高效云去除，省电98.6%。
ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
- 基于脉冲神经网络与分布式联邦学习，在卫星上直接处理图像去云。
- 每帧推理仅耗0.287mJ，比传统模型节能98.6%。
- 适合资源受限的低轨卫星群，提升灾情监测实时性。
低地球轨道（LEO）卫星为灾害监测和环境观测提供高分辨率、大范围地球观测能力。然而云层遮挡常导致地表信息丢失，传统方法需将含云图像传回地面站处理，受限于通信窗口短、星地带宽有限及高延迟。本文提出一种面向LEO星座的新型卫星联邦学习框架OrbitALIF，通过在轨训练与推理，利用一个仅有230万参数的脉冲神经网络（SNN）骨干网络，结合自适应门控融合模块（AGFM）与谱-空间混合注意力模块（SHAM），并通过星间链路实现模型权重共享。实验表明，OrbitALIF在保持良好去云质量的同时，每帧推理能耗仅0.287mJ，较等效人工神经网络（ANN）降低72.3倍（98.6%），显著提升能效。
原文摘要 · Abstract (English)
Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth's surface, and conventional cloud-removal pipelines that download cloudy images to ground stations for processing suffer from limited contact windows, constrained satellite-to-ground bandwidth, and high latency. In this work, we propose a novel satellite federated learning framework for cloud removal across LEO constellations, named orbital attention leaky integrate-and-fire (OrbitALIF). OrbitALIF performs both onboard training and inference using a compact 2.30,M-parameter spiking neural network (SNN) backbone with an adaptive gated fusion module (AGFM) and a spectral-spatial hybrid attention module (SHAM), combined with a decentralized federated learning strategy that shares model weights via inter-satellite links. Our experiments show that OrbitALIF achieves competitive cloud removal quality while consuming only 0.287,mJ per inference on neuromorphic hardware, a 72.3 times (98.6%) energy reduction versus an equivalent artificial neural network (ANN).
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