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.

翻译：小样本学习（Few-shot Learning, FSL）是图像分类领域中重要且困难的问题之一。然而，与可见光数据集的快速发展相比，SAR目标图像分类的进展明显缓慢。缺乏统一的基准是造成这一现象的关键原因，而当前文献可能严重忽视了这一点。SAR目标图像分类的研究人员通常在自己的数据集和实验设置下报告新结果，这导致结果比较效率低下，并阻碍了该领域的进一步发展。基于这一观察，我们提出了一种新颖的少量样本SAR图像分类基准（FewSAR）。FewSAR包含一个开源的Python代码库，涵盖三类共15种经典方法，用于少量样本SAR图像分类。它为不同的少量样本SAR图像分类任务提供了可访问且可定制的测试平台。为进一步理解不同少量样本方法的性能，我们建立了评估协议并在基准内开展了大量实验。通过分析相同设置下的定量结果和运行时间，我们观察到度量学习方法的准确率可达到最佳结果。元学习方法和微调方法在少量样本SAR图像上表现较差，这主要归因于现有数据集的偏差。我们相信，FewSAR将为未来在SAR图像分类与少量样本深度学习交叉领域的实际挑战研究开辟新途径。我们将提供FewSAR的代码，地址为https://github.com/solarlee/FewSAR。