The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation. Throughout the history of SAR ATR research, there have been only a number of small datasets, mainly including targets like ships, airplanes, buildings, etc. There is only one vehicle dataset MSTAR collected in the 1990s, which has been a valuable source for SAR ATR. To fill this gap, this paper introduces a large-scale, new dataset named ATRNet-STAR with 40 different vehicle categories collected under various realistic imaging conditions and scenes. It marks a substantial advancement in dataset scale and diversity, comprising over 190,000 well-annotated samples, 10 times larger than its predecessor, the famous MSTAR. Building such a large dataset is a challenging task, and the data collection scheme will be detailed. Secondly, we illustrate the value of ATRNet-STAR via extensively evaluating the performance of 15 representative methods with 7 different experimental settings on challenging classification and detection benchmarks derived from the dataset. Finally, based on our extensive experiments, we identify valuable insights for SAR ATR and discuss potential future research directions in this field. We hope that the scale, diversity, and benchmark of ATRNet-STAR can significantly facilitate the advancement of SAR ATR.

翻译：合成孔径雷达自动目标识别（SAR ATR）领域缺乏公开可用的大规模高质量数据集，这严重阻碍了快速发展的深度学习技术在该领域的应用，而该技术具有释放该领域新能力的巨大潜力。这主要是因为从SAR图像中收集大量多样化的目标样本成本极其高昂，主要受限于隐私问题、微波雷达图像感知特性以及数据标注所需的专业知识。在SAR ATR研究史上，仅有少数小型数据集，主要包括船只、飞机、建筑物等目标。仅有20世纪90年代收集的车辆数据集MSTAR，一直是SAR ATR研究的重要数据源。为填补这一空白，本文引入了一个名为ATRNet-STAR的大规模新数据集，包含在各种真实成像条件和场景下收集的40种不同车辆类别。该数据集在规模和多样性上均实现了显著提升，包含超过19万个标注良好的样本，规模是其前身著名数据集MSTAR的10倍。构建如此大规模的数据集是一项具有挑战性的任务，本文将详述数据收集方案。其次，我们通过在从该数据集衍生的具有挑战性的分类与检测基准上，以7种不同的实验设置广泛评估15种代表性方法的性能，从而阐明ATRNet-STAR的价值。最后，基于我们的大量实验，我们总结了SAR ATR领域有价值的见解，并讨论了该领域潜在的未来研究方向。我们希望ATRNet-STAR的规模、多样性和基准能够显著推动SAR ATR领域的发展。