# Sparse Signal Models for Data Augmentation in Deep Learning ATR

arXiv: https://arxiv.org/abs/2012.09284 · HTML: https://mlsanity.com/paper/2012.09284/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation](https://mlsanity.com/paper/2307.05087/) (2023)
- [SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs](https://mlsanity.com/paper/2407.00851/) (2024)
- [Neural Implicit Representations for 3D Synthetic Aperture Radar Imaging](https://mlsanity.com/paper/2602.17556/) (2026)

## Abstract (arXiv)

Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class. Recently, learning methods have shown to achieve state-of-the-art classification accuracy if abundant training data is available, sampled uniformly over the classes, and their poses. In this paper, we consider the task of ATR with a limited set of training images. We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm, such as a Convolutional neural network (CNN). The proposed data augmentation method employs a limited persistence sparse modeling approach, capitalizing on commonly observed characteristics of wide-angle synthetic aperture radar (SAR) imagery. Specifically, we exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting. Using this estimated model, we synthesize new images at poses and sub-pixel translations not available in the given data to augment CNN's training data. The experimental results show that for the training data starved region, the proposed method provides a significant gain in the resulting ATR algorithm's generalization performance.