Dense capsule network for SAR automatic target recognition with limited data

Automatic target recognition for Synthetic Aperture Radar (SAR) images is a very challengeable problem due to the lack of enough training examples. Currently, there are two options for a network to learn on small SAR data sets: to increase the depth and width or to use the Capsule Network (CapsNet). To take advantage of both options, this paper proposes a Dense CapsNet for SAR target recognition. The contributions are mainly twofold. First, we replace the original convolutional layers with dense blocks in order to increase the depth of the network. Second, we substitute deconvolutional layers for fully connected layers to build the reconstruction network, which is beneficial for improving training efficiency. The experimental results on the MSTAR data set demonstrate that our Dense CapsNet outperforms other state-of-the-art methods upon limited data.