Abstract
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.
Abstract (translated)
在现实世界场景中,并不一定能够在每个类别中收集 hundreds 个标记样本来进行深度学习为基础的SAR自动目标识别模型的训练。本工作专门解决了少量标记样本支持的兴趣任务SAR ATR问题,也就是只有极少数标记样本可用来支持感兴趣的任务。我们的方法是分为两个阶段。在第一阶段,通过自监督学习从大规模未标记SAR数据中训练了一个全局表示模型。在第二阶段,全局模型被用作固定特征提取器和分类器,以根据少量支持样本将特征空间划分为多个特征方向,同时进行校准以检测异常输入。与竞争方法不同,我们的方法从未标记数据中提取高度可移植的特征,这些特征与后续任务几乎没有关系。我们在标准扩展的MSTAR operating条件下评估了我们的方法,发现它在许多不同的少量样本设置中实现了高准确性和鲁棒性的离群值检测。我们的结果特别有意义,因为它们显示了SAR ATR全球模型方法的优点,它采取了最少的假设,并提供了许多扩展方向。
URL
https://arxiv.org/abs/2303.10800
