  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.
