Target Classification Using Spiking Neural Networks for SAR Images

Abstract

Synthetic aperture radar (SAR) imagery is widely applied to recognize target ships in marine surveillance since it can operate in all-weather day-and-night conditions. Deep learning and neural networks have also been used more frequently in recent years with SAR data and have achieved significant progress. However, it takes a lot of energy for deep neural networks to learn and hinders the further development of SAR technology. Compared with the traditional deep neural networks, Spiking Neural Networks (SNNs) have temporal information processing ability, low power consumption, and high biological plausibility, which are suitable for future radar intelligent sensing. In this paper, we take inspiration from SNNs and propose an energy-efficient method, namely SpikingSAR. The experimental results show that the proposed method outperforms the state-of-the-art deep learning methods as well as the conventional methods, while it also has fewer model parameters.