SAR Neural Radiance Fields with Multi-Resolution Hash Encoding

Synthetic Aperture Radar (SAR) possesses all-weather, all-time capabilities, but SAR imagery is highly sensitive to the observation configuration, with images from different viewing angles exhibiting significant variations. This poses challenges for the task of detection and recognition in SAR imagery. Inspired by the concept of neural radiance fields, this paper combines SAR imaging principles with neural networks. Our aim is to generate SAR images from other observation angles using a limited set of observation angles, thereby enhancing the physical interpretability of SAR neural networks. Additionally, within the SAR-NeRF framework, we introduce a multi-resolution hash encoding method. This approach divides the SAR imaging space into a multi-resolution grid, enhancing the training speed of SAR-NeRF and reducing the model size. We conducted experiments on the MSTAR dataset and performed quantitative evaluations using Peak Signal-to-Noise Ratio. The results indicate that our method not only matches or surpasses previous methods in generative performance but also boasts shorter training times and smaller model sizes, demonstrating its potential applicability.