# Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance

arXiv: https://arxiv.org/abs/1708.07920 · HTML: https://mlsanity.com/paper/1708.07920/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be](https://mlsanity.com/paper/2110.05861/) (2021)
- [Permutation-Invariant Tabular Data Synthesis](https://mlsanity.com/paper/2211.09286/) (2022)
- [New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis](https://mlsanity.com/paper/2110.15383/) (2021)
- [A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation](https://mlsanity.com/paper/2606.28896/) (2026)

## Abstract (arXiv)

This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR ATR represents the robustness against misalignment of target chips extracted from SAR images. To understand the translation invariance of the CNNs, we trained CNNs which classify the target chips from the MSTAR into the ten classes under the condition of with and without data augmentation, and then visualized the translation invariance of the CNNs. According to our results, even if we use a deep residual network, the translation invariance of the CNN without data augmentation using the aligned images such as the MSTAR target chips is not so large. A more important factor of translation invariance is the use of augmented training data. Furthermore, our CNN using augmented training data achieved a state-of-the-art classification accuracy of 99.6%. These results show an importance of domain-specific data augmentation.