Abstract. Object detection in synthetic aperture radar (SAR) images remains a challenging problem due to the particular imaging mechanism of SAR systems. The sizes of targets are relatively small and the scenes are large, indicating that the intersection over union value between the targets and anchors is probably small. In addition, SAR images are severely polluted by speckles under normal conditions. The edges of objects in SAR images are blurred. In this study, we proposed an improved object-detection framework based on a two-stage faster region-based convolutional neural network. A feature-enhancement module based on self-attention mechanism is designed to learn a target-oriented feature map, where the spatial attention and channel attention work simultaneously, such that targets can be highlighted and the speckles can be suppressed to a certain extent. Two separate feature maps serve as the input of the region proposal network to isolate the classification and regression tasks. Because only one category exists in the ship-detection task, correctly distinguishing targets from the background results in the correct detection of ships. In the final classification stage, an extended region-of-interest pooling operation is performed on the potential proposals and contexture information. The usage of extra information can improve target fine-tuning in the final network. To avoid ignoring small targets, we carefully set the anchors’ parameters based on the analysis of ground truth and select the appropriate shapes of feature maps. With the help of these modifications, the proposed method can detect small, weak, and dense targets in SAR images. Ablation experiments over networks with different configurations prove that the proposed modules are working. Experiments on real SAR Gaofen-3 and Sentinel-1 images demonstrate the efficiency of the proposed object-detection framework.

中文翻译：

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基于注意力机制和扩展池化的合成孔径雷达图像两阶段船舶检测

摘要。由于 SAR 系统的特殊成像机制，合成孔径雷达 (SAR) 图像中的目标检测仍然是一个具有挑战性的问题。目标的大小比较小，场景比较大，说明目标和anchor的union over union值可能很小。此外，SAR图像在正常情况下受到散斑的严重污染。SAR 图像中物体的边缘是模糊的。在这项研究中，我们提出了一种改进的对象检测框架，该框架基于两阶段更快的基于区域的卷积神经网络。设计了一个基于自注意力机制的特征增强模块来学习面向目标的特征图，其中空间注意力和通道注意力同时工作，从而可以突出目标并在一定程度上抑制斑点。两个独立的特征图作为区域提议网络的输入，以隔离分类和回归任务。因为在船舶检测任务中只存在一个类别，正确区分目标和背景会导致正确检测船舶。在最后的分类阶段，对潜在提议和上下文信息执行扩展的兴趣区域池化操作。额外信息的使用可以改善最终网络中的目标微调。为了避免忽略小目标，我们根据地面实况分析仔细设置锚点的参数，并选择合适的特征图形状。在这些修改的帮助下，所提出的方法可以检测小、弱、和 SAR 图像中的密集目标。在具有不同配置的网络上进行的消融实验证明所提出的模块正在工作。在真实 SAR Gaofen-3 和 Sentinel-1 图像上的实验证明了所提出的目标检测框架的效率。