Abstract
The Images generated by high-resolution Synthetic Aperture Radar (SAR) have vast areas of application as they can work better in adverse light and weather conditions. One such area of application is in the military systems. This study is an attempt to explore the suitability of current state-of-the-art models introduced in the domain of computer vision for SAR target classification (Moving and Stationary Target Acquisition and Recognition (MSTAR)). Since the application of any solution produced for military systems would be strategic and real-time, accuracy is often not the only criterion to measure its performance. Other important parameters like prediction time and input resiliency are equally important. The paper deals with these issues in the context of SAR images. Experimental results show that deep learning models can be suitably applied in the domain of SAR image classification with the desired performance levels.
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Acknowledgements
This work is partly supported by extramural research grant from Ministry of Electronics and Information Technology, Govt. of India (grant no: 3(9)/2021-EG-II) and by HPE Aruba Centre for Research in Information Systems at BHU (No. M-22-69 of BHU).
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Singh, A., Singh, V.K. (2023). Exploring Deep Learning Methods for Classification of Synthetic Aperture Radar Images: Towards NextGen Convolutions via Transformers. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2022. Communications in Computer and Information Science, vol 1798. Springer, Cham. https://doi.org/10.1007/978-3-031-28183-9_18
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