Abstract
Synthetic Aperture Radar (SAR), with its all- weather and wide-area observation capabilities, serves as a crucial tool for wake detection. However, due to its complex imaging mechanism, wake features in SAR images often appear abstract and noisy, posing challenges for accurate annotation. In contrast, optical images provide more distinct visual cues, but models trained on optical data suffer from performance degradation when applied to SAR images due to domain shift. To address this cross-modal domain adaptation challenge, we propose a Similarity-Guided and Memory-Guided Domain Adap- tation (termed SimMemDA) framework for unsupervised domain adaptive ship wake detection via instance-level feature similarity filtering and feature memory guidance. Specifically, to alleviate the visual discrepancy between optical and SAR images, we first utilize WakeGAN to perform style transfer on optical images, generating pseudo-images close to the SAR style. Then, instance-level feature similarity filtering mechanism is designed to identify and prioritize source samples with target-like dis- tributions, minimizing negative transfer. Meanwhile, a Feature- Confidence Memory Bank combined with a K-nearest neighbor confidence-weighted fusion strategy is introduced to dynamically calibrate pseudo-labels in the target domain, improving the reliability and stability of pseudo-labels. Finally, the framework further enhances generalization through region-mixed training, strategically combining source annotations with calibrated tar- get pseudo-labels. Experimental results demonstrate that the proposed SimMemDA method can improve the accuracy and robustness of cross-modal ship wake detection tasks, validating the effectiveness and feasibility of the proposed method.
Abstract (translated)
合成孔径雷达（SAR）凭借其全天候和广域观测能力，成为舰船尾流检测的重要工具。然而，由于其复杂的成像机制，SAR图像中的尾流特征往往显得抽象且嘈杂，这给准确标注带来了挑战。相比之下，光学影像提供了更为清晰的视觉线索，但基于光学数据训练出的模型在应用于SAR影像时会因领域差异而性能下降。为了应对跨模态领域的适应性挑战，我们提出了一种以相似度引导和记忆引导的域自适应框架（简称SimMemDA），用于通过实例级特征相似度过滤及特征记忆引导来实现无监督的跨域舰船尾流检测。具体来说，为了解决光学与SAR影像之间的视觉差异问题，我们首先利用WakeGAN对光学图像进行风格转换，生成接近于SAR样式的真实伪图。随后设计了实例级别的特征相似性过滤机制以识别并优先选择分布类似于目标领域的源样本，从而减少负向迁移现象的发生。同时引入结合K近邻权重融合策略的特征置信度记忆库，用于动态校准目标领域中的伪标签，提高其可靠性和稳定性。最后，该框架通过区域混合训练进一步提升了模型的泛化能力，将源标注与校准后的目标伪标签有策略地结合起来。实验结果表明所提出的SimMemDA方法能够提升跨模态舰船尾流检测任务的准确度和鲁棒性，验证了其有效性和可行性。
URL
https://arxiv.org/abs/2509.12279
