SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation

by   Zhengxin Lei, et al.
FUDAN University

SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6% can be achieved by using only 12 images per class.


page 1

page 7

page 8

page 9

page 10

page 11

page 12


Differentiable SAR Renderer and SAR Target Reconstruction

Forward modeling of wave scattering and radar imaging mechanisms is the ...

HRTF Field: Unifying Measured HRTF Magnitude Representation with Neural Fields

Head-related transfer functions (HRTFs) are a set of functions describin...

DGP-Net: Dense Graph Prototype Network for Few-Shot SAR Target Recognition

The inevitable feature deviation of synthetic aperture radar (SAR) image...

SSN: Stockwell Scattering Network for SAR Image Change Detection

Recently, synthetic aperture radar (SAR) image change detection has beco...

SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks

The outstanding pattern recognition performance of deep learning brings ...

Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense

Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automat...

Joint Embedding and Classification for SAR Target Recognition

Deep learning can be an effective and efficient means to automatically d...

Please sign up or login with your details

Forgot password? Click here to reset