  Few-shot learning (FSL) is one of the significant and hard problems in the
field of image classification. However, in contrast to the rapid development of
the visible light dataset, the progress in SAR target image classification is
much slower. The lack of unified benchmark is a key reason for this phenomenon,
which may be severely overlooked by the current literature. The researchers of
SAR target image classification always report their new results on their own
datasets and experimental setup. It leads to inefficiency in result comparison
and impedes the further progress of this area. Motivated by this observation,
we propose a novel few-shot SAR image classification benchmark (FewSAR) to
address this issue. FewSAR consists of an open-source Python code library of 15
classic methods in three categories for few-shot SAR image classification. It
provides an accessible and customizable testbed for different few-shot SAR
image classification task. To further understanding the performance of
different few-shot methods, we establish evaluation protocols and conduct
extensive experiments within the benchmark. By analyzing the quantitative
results and runtime under the same setting, we observe that the accuracy of
metric learning methods can achieve the best results. Meta-learning methods and
fine-tuning methods perform poorly on few-shot SAR images, which is primarily
due to the bias of existing datasets. We believe that FewSAR will open up a new
avenue for future research and development, on real-world challenges at the
intersection of SAR image classification and few-shot deep learning. We will
provide our code for the proposed FewSAR at https://github.com/solarlee/FewSAR.
