Shape deformation of targets in SAR image due to random orientation and partial information loss caused by occlusion of the radar signal, is an essential challenge in SAR ship detection. In this paper, we propose a data augmentation method to train a deep network that is robust to partial information loss within the targets. Taking advantage of ground-truth annotations for bounding box and instance segmentation mask, we present a simple and effective pipeline to simulate information loss on targets in instance-level, while preserving contextual information. Furthermore, we adopt deformable convolutional network to adaptively extract shape-invariant deep features from geometrically translated targets. By learning sampling offset to the grid of standard convolution, the network can robustly extract the features from targets with shape variations for SAR ship detection. Experiments on the HRSID dataset including comparisons with other deep networks and augmentation methods, as well as ablation study, demonstrate the effectiveness of our proposed method.
翻译:由于随机定向和雷达信号的封闭造成的部分信息损失,合成孔径雷达图像中目标变形的形状变形是合成孔径雷达船舶探测的一项基本挑战。在本文件中,我们提出一种数据增强方法,以训练一个在目标范围内强大到部分信息损失的深网络。利用地面真相说明来捆绑框和实例分解面罩,我们提出了一个简单而有效的管道,以模拟在实例一级的目标信息损失,同时保存背景信息。此外,我们采用了可变变动网络,以适应性地从几何变换目标中提取形状-差异深度特征。通过对标准变异网进行取样,该网络可以有力地从目标中提取特征,并进行合成孔径雷达船舶探测的形状变异。在HRSID数据集上进行实验,包括与其他深网络和增强方法进行比较,以及进行模拟研究,展示了我们拟议方法的有效性。