This article delves into the topic of unsupervised domain adaptation (UDA) by transferring knowledge from rich labeled optical domain to unlabeled synthetic aperture radar (SAR) domain, tackling the issues faced by deep-learning-based SAR ship classification methods that rely on abundant labeled SAR images. Typical UDA methods usually extract domain-invariant representations (DIRs) between two domains. However, due to the prominent differences in imaging mechanisms between optical and SAR images, the discriminative characteristics of same classes across domains may vary. Feature representation guided by labeled optical images therefore suffers from a particularly serious source-bias problem, making DIR difficult to be extracted. Moreover, capturing the category structure of the target domain is crucial for classification tasks. To solve the above challenges, this article proposes a UDA framework for SAR ship classification via progressive feature alignment between optical and unlabeled SAR domains, gradually aligning two domains across domain and class levels. At the domain level, to reduce the transfer difficulty stemming from the prominent differences between SAR and optical images, feature calibrated domain alignment (FCDA) is presented to achieve accurate DIR extraction. FCDA combines the reconstruction and the consistency constraints of different perturbed versions of the same image to calibrate the optical-bias representation into the features of unbiased toward a specific domain. At the class level, we proposed feature enhanced class alignment (FECA) to capture the fine-grained category structure of the SAR domain. FECA incorporates pseudo-label-based cross-domain contrastive learning (CDC) for intraclass compactness as well as interclass separation among cross-domain categories, along with a consistency learning approach to enhance the class structure of SAR domain. The experimental results indicate that our method achieves exceptional performance in unsupervised classification of SAR ships.

中文翻译：

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通过渐进式特征对齐进行船舶分类的无监督域适应：从光学到 SAR 图像


本文深入研究了无监督域适应（UDA）主题，通过将知识从丰富的标记光学域转移到未标记的合成孔径雷达（SAR）域，解决了基于深度学习的SAR船舶分类方法所面临的问题，该方法依赖于丰富的标记SAR 图像。典型的 UDA 方法通常提取两个域之间的域不变表示 (DIR)。然而，由于光学图像和SAR图像之间成像机制的显着差异，跨领域的相同类别的判别特征可能会有所不同。因此，由标记光学图像引导的特征表示遭受特别严重的源偏差问题，使得 DIR 难以提取。此外，捕获目标域的类别结构对于分类任务至关重要。为了解决上述挑战，本文提出了一种用于 SAR 船舶分类的 UDA 框架，通过光学和未标记 SAR 域之间的渐进式特征对齐，跨域和类级别逐步对齐两个域。在域层面，为了降低由于SAR和光学图像之间的显着差异而导致的传输难度，提出了特征校准域对齐（FCDA）来实现精确的DIR提取。 FCDA结合了同一图像的不同扰动版本的重建和一致性约束，将光学偏差表示校准为对特定域无偏差的特征。在类级别，我们提出了特征增强类对齐（FECA）来捕获 SAR 域的细粒度类别结构。 FECA 结合了基于伪标签的跨域对比学习 (CDC)，以实现类内紧凑性以及跨域类别之间的类间分离，并采用一致性学习方法来增强 SAR 域的类结构。实验结果表明，我们的方法在SAR船舶的无监督分类中取得了优异的性能。