Zhao, G.; Li, P.; Zhang, Z.; Guo, F.; Huang, X.; Xu, W.; Wang, J.; Chen, J. Towards SAR Automatic Target Recognition Multi-Category SAR Image Classification Based on Light Weight Vision Transformer. Preprints2024, 2024070068. https://doi.org/10.20944/preprints202407.0068.v1
APA Style
Zhao, G., Li, P., Zhang, Z., Guo, F., Huang, X., Xu, W., Wang, J., & Chen, J. (2024). Towards SAR Automatic Target Recognition Multi-Category SAR Image Classification Based on Light Weight Vision Transformer. Preprints. https://doi.org/10.20944/preprints202407.0068.v1
Chicago/Turabian Style
Zhao, G., Jinyin Wang and Jianlong Chen. 2024 "Towards SAR Automatic Target Recognition Multi-Category SAR Image Classification Based on Light Weight Vision Transformer" Preprints. https://doi.org/10.20944/preprints202407.0068.v1
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 (ATR) 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 (RNN) and convolutional neural network (CNN)-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 (LViT)-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.
Keywords
Multi-category learning; Lightweight vision transformer (LViT); Synthetic aperture radar (SAR); Automatic target recognition (ATR); Open set recognition (OSR)
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
