DL based Synthetic Aperture Radar (SAR) ship detection has tremendous advantages in numerous areas. However, it still faces some problems, such as the lack of prior knowledge, which seriously affects detection accuracy. In order to solve this problem, we propose a scene-aware SAR ship detection method based on unsupervised sea-land segmentation. This method follows a classical two-stage framework and is enhanced by two models: the unsupervised land and sea segmentation module (ULSM) and the land attention suppression module (LASM). ULSM and LASM can adaptively guide the network to reduce attention on land according to the type of scenes (inshore scene and offshore scene) and add prior knowledge (sea land segmentation information) to the network, thereby reducing the network's attention to land directly and enhancing offshore detection performance relatively. This increases the accuracy of ship detection and enhances the interpretability of the model. Specifically, in consideration of the lack of land sea segmentation labels in existing deep learning-based SAR ship detection datasets, ULSM uses an unsupervised approach to classify the input data scene into inshore and offshore types and performs sea-land segmentation for inshore scenes. LASM uses the sea-land segmentation information as prior knowledge to reduce the network's attention to land. We conducted our experiments using the publicly available SSDD dataset, which demonstrated the effectiveness of our network.

翻译：基于深度学习的合成孔径雷达（SAR）舰船检测在众多领域具有显著优势，但依然面临先验知识缺乏等影响检测精度的问题。为解决此问题，本文提出一种基于无监督海陆分割的场景感知SAR舰船检测方法。该方法遵循经典两阶段框架，并通过无监督海陆分割模块（ULSM）与陆地注意力抑制模块（LASM）进行增强。ULSM与LASM能够根据场景类型（近岸场景与离岸场景）自适应引导网络降低对陆地的关注度，并将先验知识（海陆分割信息）融入网络，从而直接减少网络对陆地的注意力，相对提升离岸检测性能。此举既提高了舰船检测精度，也增强了模型的可解释性。具体而言，针对现有基于深度学习的SAR舰船检测数据集中缺乏海陆分割标签的问题，ULSM采用无监督方法将输入数据场景分类为近岸与离岸类型，并对近岸场景执行海陆分割。LASM则利用海陆分割信息作为先验知识以降低网络对陆地的关注。我们在公开可用的SSDD数据集上进行了实验，验证了所提网络的有效性。