Recent years have witnessed great progress in synthetic aperture radar (SAR) target detection methods based on deep learning. However, these methods generally assume the training data and test data obey the same distribution, which does not always hold when the radar parameters, imaging algorithm, viewpoints, scenes, etc., change in practice. When such a distribution mismatch occurs, it will cause a significant performance drop. Domain adaptation methods provide an effective way to address this problem by transferring knowledge from the source domain (training data) to the target domain (test data). In this article, we proposed an unsupervised faster R-CNN SAR target detection framework based on domain adaptation, which can improve SAR target detection performance in the unlabeled target domain by borrowing the knowledge of the labeled source domain. Our approach is composed of the following three stages: pixel-domain adaptation (PDA), multilevel feature domain adaptation (MFDA), and iterative pseudolabeling (IPL). By generating transition domain using generative adversarial networks, the PDA stage can reduce the appearance differences of SAR images. At the MFDA stage, the detector can not only learn the domain-invariant global features and instance-level regional features via multilevel adversarial learning in the common feature space but also reweight the low-level global features according to their relative importance to the target domain. At the IPL stage, we design an iterative pseudo labeling strategy that can select pseudo-labels on instance level and image level to encourage the detector to learn more discriminative features of the target domain directly. We evaluate our method using miniSAR and FARADSAR datasets. The experimental results demonstrate the effectiveness of the proposed unsupervised domain adaptation target detection approach.

中文翻译：

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用于 SAR 目标检测的无监督域适应


近年来，基于深度学习的合成孔径雷达（SAR）目标检测方法取得了巨大进展。然而，这些方法通常假设训练数据和测试数据服从相同的分布，当实际中雷达参数、成像算法、视点、场景等发生变化时，这种分布并不总是成立。当出现这样的分布不匹配时，将会导致性能的显着下降。域适应方法通过将知识从源域（训练数据）转移到目标域（测试数据）提供了解决此问题的有效方法。在本文中，我们提出了一种基于域自适应的无监督快速R-CNN SAR目标检测框架，该框架可以通过借用标记源域的知识来提高未标记目标域中的SAR目标检测性能。我们的方法由以下三个阶段组成：像素域适应（PDA）、多级特征域适应（MFDA）和迭代伪标记（IPL）。通过使用生成对抗网络生成过渡域，PDA 阶段可以减少 SAR 图像的外观差异。在MFDA阶段，检测器不仅可以通过公共特征空间中的多级对抗学习来学习域不变的全局特征和实例级区域特征，还可以根据低级全局特征对目标域的相对重要性重新加权。在IPL阶段，我们设计了一种迭代伪标记策略，可以在实例级别和图像级别选择伪标签，以鼓励检测器直接学习目标域的更多判别特征。我们使用 miniSAR 和 FARADSAR 数据集评估我们的方法。 实验结果证明了所提出的无监督域自适应目标检测方法的有效性。