# Exploring Deep Learning Methods for Classification of SAR Images: Towards NextGen Convolutions via Transformers

Source: [Exploring Deep Learning Methods for Classification of SAR Images: Towards NextGen Convolutions via Transformers](<https://www.sota2.com/research/paper/68fa6ee01e209131cea42941>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:08:40.604Z

Authors: Aakash Singh, Vivek Kumar Singh

Published: 2023-03-28T09:43:58.000Z

## Abstract

Images generated by high-resolution 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 (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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.15852>)

[PDF](<https://arxiv.org/pdf/2303.15852.pdf>)