Detecting ships in synthetic aperture radar (SAR) images poses a formidable challenge, primarily attributed to limited observation samples and complex environments. To address this problem, driven by latent similarity between optical and SAR images, we propose a domain-adaptive few-shot detection algorithm for SAR ship detection [single shot multibox detector (SSD)]. The algorithm requires only a few training samples of SAR images and effectively combines them with rich optical images to utilize domain information. First, we develop an efficient plug-and-play distance metric function. This function accurately measures the distances between features from the optical domain and the SAR domain. Second, we design a lossy branching mechanism to effectively utilize SAR domain knowledge. This branching mechanism is driven by the observed latent similarity in domain knowledge distribution between optical and SAR images. In addition, we introduce a dual-stream branching feature alignment extraction network with weight sharing. This network architecture enables better knowledge extraction and sharing between optical and SAR domains. To evaluate our method, we conducted experiments on a newly created dataset, DIOR2SSDD, which is designed for few-shot SAR image ship detections across optical and SAR domains. The experimental results show that under three-, five-, and ten-shot settings, the mean average precision (mAP) of our method can reach 59.2%, 61.2%, and 64.6%, and with only 10% SAR training data, the mAP can reach 89.3%. It indicates that our method can effectively transfer domain knowledge and achieve excellent ship detection performance in SAR images.

中文翻译：

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光学与SAR图像潜在相似性驱动的域自适应小镜头SAR船舶检测算法


在合成孔径雷达（SAR）图像中检测船舶提出了巨大的挑战，这主要归因于有限的观测样本和复杂的环境。为了解决这个问题，在光学图像和 SAR 图像之间潜在相似性的驱动下，我们提出了一种用于 SAR 船舶检测的域自适应少样本检测算法 [单样本多框检测器 (SSD)]。该算法只需要少量的SAR图像训练样本，并将其与丰富的光学图像有效地结合起来，利用域信息。首先，我们开发了一种高效的即插即用距离度量函数。该功能可准确测量光域和 SAR 域特征之间的距离。其次，我们设计了一种有损分支机制来有效利用 SAR 领域知识。这种分支机制是由光学图像和 SAR 图像之间领域知识分布中观察到的潜在相似性驱动的。此外，我们引入了具有权重共享的双流分支特征对齐提取网络。这种网络架构可以更好地提取知识并在光学和 SAR 领域之间共享。为了评估我们的方法，我们在新创建的数据集 DIOR2SSDD 上进行了实验，该数据集专为跨光学和 SAR 领域的少镜头 SAR 图像船舶检测而设计。实验结果表明，在3次、5次和10次射击设置下，我们的方法的平均精度（mAP）可以达到59.2％、61.2％和64.6％，并且仅用10％的SAR训练数据， mAP可以达到89.3%。这表明我们的方法可以有效地传递领域知识并在SAR图像中实现出色的船舶检测性能。