  Convolutional neural networks (CNN) have made great progress for synthetic
aperture radar (SAR) images change detection. However, sampling locations of
traditional convolutional kernels are fixed and cannot be changed according to
the actual structure of the SAR images. Besides, objects may appear with
different sizes in natural scenes, which requires the network to have stronger
multi-scale representation ability. In this paper, a novel
\underline{D}eformable \underline{R}esidual Convolutional Neural
\underline{N}etwork (DRNet) is designed for SAR images change detection. First,
the proposed DRNet introduces the deformable convolutional sampling locations,
and the shape of convolutional kernel can be adaptively adjusted according to
the actual structure of ground objects. To create the deformable sampling
locations, 2-D offsets are calculated for each pixel according to the spatial
information of the input images. Then the sampling location of pixels can
adaptively reflect the spatial structure of the input images. Moreover, we
proposed a novel pooling module replacing the vanilla pooling to utilize
multi-scale information effectively, by constructing hierarchical residual-like
connections within one pooling layer, which improve the multi-scale
representation ability at a granular level. Experimental results on three real
SAR datasets demonstrate the effectiveness of the proposed DRNet.
