Automatic Target Recognition (ATR) from Synthetic Aperture Radar (SAR) data covers a wide range of applications. SAR ATR helps to detect and track vehicles and other objects, e.g. in disaster relief and surveillance operations. Aircraft classification covers a significant part of this research area, which differs from other SAR-based ATR tasks, such as ship and ground vehicle detection and classification, in that aircrafts are usually a static target, often remaining at the same location and in a given orientation for longer time frames. Today, there is a significant mismatch between the abundance of deep learning-based aircraft classification models and the availability of corresponding datasets. This mismatch has led to models with improved classification performance on specific datasets, but the challenge of generalizing to conditions not present in the training data (which are expected to occur in operational conditions) has not yet been satisfactorily analyzed. This paper aims to evaluate how classification performance and generalization capabilities of deep learning models are influenced by the diversity of the training dataset. Our goal is to understand the model’s competence and the conditions under which it can achieve proficiency in aircraft classification tasks for high-resolution SAR images while demonstrating generalization capabilities when confronted with novel data that include different geographic locations, environmental conditions, and geometric variations. We address this gap by using manually annotated high-resolution SAR data from TerraSAR-X and TanDEM-X and show how the classification performance changes for different application scenarios requiring different training and evaluation setups. We find that, as expected, the type of aircraft plays a crucial role in the classification problem, since it will vary in shape and dimension. However, these aspects are secondary to how the SAR image is acquired, with the acquisition geometry playing the primary role. Therefore, we find that the characteristics of the acquisition are much more relevant for generalization than the complex geometry of the target. We show this for various models selected among the standard classification algorithms.

中文翻译：

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SAR 影像基于深度学习的飞机分类中的泛化


合成孔径雷达 （SAR） 数据的自动目标识别 （ATR） 涵盖广泛的应用。SAR ATR 有助于检测和跟踪车辆和其他物体，例如在救灾和监视行动中。飞机分类涵盖了该研究领域的重要组成部分，它与其他基于 SAR 的 ATR 任务（例如船舶和地面车辆检测和分类）不同，因为飞机通常是静态目标，通常在同一位置和给定方向停留更长的时间范围。如今，基于深度学习的飞机分类模型的丰富性与相应数据集的可用性之间存在着明显的不匹配。这种不匹配导致模型在特定数据集上的分类性能得到提高，但推广到训练数据中不存在的条件（预计在操作条件下发生）的挑战尚未得到令人满意的分析。本文旨在评估深度学习模型的分类性能和泛化能力如何受到训练数据集多样性的影响。我们的目标是了解该模型的能力和条件，以及它可以熟练完成高分辨率 SAR 图像的飞机分类任务的条件，同时在面对包括不同地理位置、环境条件和几何变化在内的新数据时展示泛化能力。我们通过使用来自 TerraSAR-X 和 TanDEM-X 的手动注释高分辨率 SAR 数据来解决这一差距，并展示了需要不同训练和评估设置的不同应用场景的分类性能如何变化。 我们发现，正如预期的那样，飞机类型在分类问题中起着至关重要的作用，因为它的形状和尺寸会有所不同。但是，这些方面对于 SAR 图像的获取方式来说是次要的，采集几何结构起着主要作用。因此，我们发现 acquisition 的特征比目标的复杂几何形状更与泛化相关。我们为在标准分类算法中选择的各种模型显示了这一点。