SAR Automatic Target Recognition Based on Supervised Deep Variational Autoencoding Model

Deep learning has been gradually used to solve SAR image classification problems for its desired performance on various recognition problems. A deep variational autoencoding model (DVAEM), that constructs a multi-stochastic-layer generative network (decoder) and variational inference network (encoder), can be employed to build a flexible and interpretable model for the SAR image target recognition task. It is scalable in the training phase and fast in the testing stage, and can extract the hierarchical structured and interpretable features from SAR images. However, the current DVAEM extracts the features of SAR images unsupervisedly, without incorporating the label information, and may fail to extract discriminative representations for the recognition task. In this article, to jointly model SAR images and their corresponding labels, we further propose supervised DVAEM with Euclidean distance restriction (rs-DVAEM), which enhances the discriminative power of latent representations of SAR images. Notably, our proposed rs-DVAEM combines the flexibility of DVAEM in describing the SAR images and the discriminative power of supervised models. Experimental results on the moving and stationary target acquisition and recognition public dataset demonstrate the effectiveness of the proposed method.