ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal

2026-08-25

AI Summary

本文提出了一种名为OrbitALIF的卫星联邦学习框架，使用紧凑的SNN模型结合分散式学习策略解决低轨卫星云层遮挡问题。

Abstract

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).

Problem

• LEO satellites
• cloud coverage
• Earth observation
• ground stations
• bandwidth

Innovation

• OrbitALIF
• spiking neural network (SNN)
• adaptive gated fusion module (AGFM)
• spectral-spatial hybrid attention module (SHAM)
• decentralized federated learning