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

arXiv: https://arxiv.org/abs/2506.20782 · HTML: https://mlsanity.com/paper/2506.20782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning](https://mlsanity.com/paper/2401.04491/) (2024)
- [Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods](https://mlsanity.com/paper/2405.04289/) (2024)
- [Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives](https://mlsanity.com/paper/2409.08290/) (2024)
- [Roadmap to Neuromorphic Computing with Emerging Technologies](https://mlsanity.com/paper/2407.02353/) (2024)
- [Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification](https://mlsanity.com/paper/2211.04297/) (2022)
- [Directly Training Temporal Spiking Neural Network with Sparse Surrogate Gradient](https://mlsanity.com/paper/2406.19645/) (2024)
- [Environmental Burden of United States Data Centers in the Artificial Intelligence Era](https://mlsanity.com/paper/2411.09786/) (2024)
- [Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement](https://mlsanity.com/paper/2208.03524/) (2022)

## Abstract (arXiv)

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.