Towards SAR Automatic Target Recognition MultiCategory SAR Image Classification Based on Light Weight Vision Transformer

TL;DR

This paper introduces a lightweight vision transformer model for SAR automatic target recognition, demonstrating improved accuracy and robustness over traditional CNN-based methods without using convolutional layers.

Contribution

It applies a novel lightweight vision transformer to SAR ATR, achieving better performance and efficiency compared to traditional CNN models.

Findings

The model achieves higher classification accuracy.

The approach is robust across different SAR datasets.

No convolutional layers are used in the model.

Abstract

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large scene data intensive technology puts a high value on automatic target recognition which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network and convolutional neural network varied models to deepen the regime's understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer based model to classify SAR images. The entire structure was verified by an open-accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.