With the rapid development of artificial neural network (ANN), the field of synthetic aperture radar (SAR) target recognition has witnessed significant progress. However, due to the poor interpretability and ease of being affected by speckle noise, it brings challenges to ANN for SAR target recognition. Spiking Neural Network (SNN) has emerged as the third-generation neural network architecture and presents promising prospects for various applications. This study aims to explore the performance of SNN in SAR target recognition. In our experiments, we achieved comparable performance to conventional neural networks by utilizing directly trained SNN. This indicates the effectiveness of SNN in coping with SAR target recognition tasks. Moreover, we investigated the impact of different spiking encoders on SAR target recognition. Specifically, we compared the performance of SNN using the Poisson encoder and utilizing the first layer of the SNN as an encoder. This comparison provides valuable insights into the optimal coding strategy for SNN-based SAR target recognition. Additionally, we examined the robustness of SNNs in the presence of strong speckle noise. Our findings demonstrate that SNN can maintain good performance under the influence of strong speckle noise. The outcomes of this research shed light on the potential of SNN as a powerful tool for SAR target recognition. Future studies can focus on exploring SNN’s applicability to SAR Interpretation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.