用脉冲神经网络解决雷达相位解缠，节能超100倍。
Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing
- 设计专用于包裹相位数据的脉冲编码与空间传播架构。
- 理论证明可实现30-100倍能效提升，精度相当。
- 适合大规模遥感数据处理与低功耗计算场景。
本文首次建立脉冲神经网络（SNN）应用于合成孔径雷达（SAR）干涉相位解缠的理论框架。尽管两个领域研究深入，但现有文献表明SNN尚未被用于相位解缠，存在显著方法空白。随着地球观测数据量呈指数增长（如NISAR任务预计两年生成100PB数据），节能处理对数据中心可持续运行至关重要。SNN凭借事件驱动计算模型，相比传统方法可实现30-100倍的能效提升，同时保持相近精度。我们提出专为包裹相位数据设计的脉冲编码方案，构建利用相位解缠空间传播特性的SNN架构，并提供计算复杂度与收敛性理论分析。结果表明，SNN固有的时序动态可自然建模相位解缠中的空间连续性约束。该工作开辟了类脑计算与雷达干涉领域的交叉新方向，为大规模InSAR处理提供可持续的补充解决方案。
原文摘要 · Abstract (English)
We present the first theoretical framework for applying spiking neural networks (SNNs) to synthetic aperture radar (SAR) interferometric phase unwrapping. Despite extensive research in both domains, our comprehensive literature review confirms that SNNs have never been applied to phase unwrapping, representing a significant gap in current methodologies. As Earth observation data volumes continue to grow exponentially (with missions like NISAR expected to generate 100PB in two years) energy-efficient processing becomes critical for sustainable data center operations. SNNs, with their event-driven computation model, offer potential energy savings of 30-100x compared to conventional approaches while maintaining comparable accuracy. We develop spike encoding schemes specifically designed for wrapped phase data, propose SNN architectures that leverage the spatial propagation nature of phase unwrapping, and provide theoretical analysis of computational complexity and convergence properties. Our framework demonstrates how the temporal dynamics inherent in SNNs can naturally model the spatial continuity constraints fundamental to phase unwrapping. This work opens a new research direction at the intersection of neuromorphic computing and SAR interferometry, offering a complementary approach to existing algorithms that could enable more sustainable large-scale InSAR processing.
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