In real-world scenarios, it may not always be possible to collect hundreds of labeled samples per class for training deep learning-based SAR Automatic Target Recognition (ATR) models. This work specifically tackles the few-shot SAR ATR problem, where only a handful of labeled samples may be available to support the task of interest. Our approach is composed of two stages. In the first, a global representation model is trained via self-supervised learning on a large pool of diverse and unlabeled SAR data. In the second stage, the global model is used as a fixed feature extractor and a classifier is trained to partition the feature space given the few-shot support samples, while simultaneously being calibrated to detect anomalous inputs. Unlike competing approaches which require a pristine labeled dataset for pretraining via meta-learning, our approach learns highly transferable features from unlabeled data that have little-to-no relation to the downstream task. We evaluate our method in standard and extended MSTAR operating conditions and find it to achieve high accuracy and robust out-of-distribution detection in many different few-shot settings. Our results are particularly significant because they show the merit of a global model approach to SAR ATR, which makes minimal assumptions, and provides many axes for extendability.

翻译：在现实场景中，为训练基于深度学习的SAR自动目标识别(ATR)模型，每类目标通常难以收集数百个带标签样本。本文专门解决少样本SAR ATR问题，即仅有少量带标签样本可用于支撑目标任务。我们的方法包含两个阶段：第一阶段，通过自监督学习在大规模多样化无标签SAR数据池上训练全局表征模型；第二阶段，将全局模型作为固定特征提取器，训练分类器以根据少样本支持样本划分特征空间，同时对其进行校准以检测异常输入。不同于依赖元学习并需要完整带标签数据集进行预训练的竞争方法，我们的方法从与下游任务几乎无关的无标签数据中学习高度可迁移的特征。我们在标准和扩展MSTAR操作条件下评估该方法，发现其在多种少样本设置中实现了高精度和鲁棒的分布外检测。研究结果的显著意义在于：证明了全局模型方法在SAR ATR中的价值——该方法假设要求极低，且具有多维度可扩展性。