  Reliable automatic target segmentation in Synthetic Aperture Radar (SAR)
imagery has played an important role in the SAR fields. Different from the
traditional methods, Spectral Residual (SR) and CFAR detector, with the recent
adavance in machine learning theory, there has emerged a novel method for SAR
target segmentation, based on the deep learning networks. In this paper, we
proposed a deep deformable residual learning network for target segmentation
that attempts to preserve the precise contour of the target. For this, the
deformable convolutional layers and residual learning block are applied, which
could extract and preserve the geometric information of the targets as much as
possible. Based on the Moving and Stationary Target Acquisition and Recognition
(MSTAR) data set, experimental results have shown the superiority of the
proposed network for the precise targets segmentation.
