标题： 用于SAR干涉相位解缠的脉冲神经网络：一种节能处理的理论框架 显示英文标题

标题： Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing

摘要： 我们提出了第一个将脉冲神经网络（SNNs）应用于合成孔径雷达（SAR）干涉相位解缠的理论框架。 尽管在两个领域都有大量研究，但我们的全面文献综述确认，SNNs 从未被用于相位解缠，这代表了当前方法中的一个重大空白。 随着地球观测数据量的指数级增长（例如，NISAR 任务预计两年内将生成 100PB 的数据），节能处理对于可持续的数据中心运营变得至关重要。 SNNs 具有事件驱动的计算模型，与传统方法相比，有望节省 30-100 倍的能量，同时保持相当的准确性。 我们开发了专门针对包裹相位数据的脉冲编码方案，提出了利用相位解缠空间传播特性的 SNN 架构，并提供了对计算复杂性和收敛性质的理论分析。 我们的框架展示了 SNN 中固有的时间动态如何自然地建模相位解缠中基本的空间连续性约束。 这项工作在神经形态计算和 SAR 干涉测量的交叉领域开辟了一个新的研究方向，为现有算法提供了一种补充方法，可能实现更可持续的大规模 InSAR 处理。 显示英文摘要

摘要： 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.