SAR-TinySNN: A Lightweight Spiking Neural Network for SAR Target Recognition

被引：0

作者：

Wang, Junyu ^{[1
]}

Sun, Hao ^{[1
]}

Sun, Yuli ^{[1
]}

Tang, Tao ^{[1
]}

Lei, Lin ^{[1
]}

Ji, Kefeng ^{[1
]}

机构：

[1] National University of Defense Technology, State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, Changsha,410073, China

来源：

IEEE Geoscience and Remote Sensing Letters | 2025年 / 22卷

关键词：

Deep neural networks - Network coding - Radar imaging - Radar target recognition;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Spiking neural networks (SNNs) are the third generation of neural networks that offer the advantages of low computational requirements, fast inference speed, and strong biological interpretability. This makes SNNs suitable for synthetic aperture radar (SAR) target recognition tasks, which are often constrained by limited computational power. This letter proposes SAR-TinySNN, a lightweight SNN architecture designed for SAR target recognition. Unlike existing SAR-related studies that predominantly rely on rate coding, SAR-TinySNN uses direct coding to encode SAR images, allowing for a more efficient coding method adapted to SAR images and achieving high target recognition accuracy, especially in scenarios with limited training samples. By integrating direct coding into a trainable SNN framework, SAR-TinySNN achieves competitive performance compared with traditional deep neural networks (DNNs) and deep SNNs on vehicle, aircraft, and ship SAR target recognition datasets, with faster inference times. The experimental results demonstrate the effectiveness of SAR-TinySNN for SAR target recognition. © 2004-2012 IEEE.

引用