Causal Adversarial Autoencoder for Disentangled SAR Image Representation and Few-Shot Target Recognition

Abstract

Lack of interpretability and weak generalization ability have become the major challenges with data-driven intelligent SAR-ATR technology, especially in practical applications with azimuth-sparse training samples. A novel insight into SAR image representation with neural networks from a causal perspective is presented in this paper. Firstly, a causal model of SAR image representation conditioned on disentangled semantic factors is proposed. A set of SAR images is considered as a low-dimensional manifold, which is controlled by three semantic factors, namely, intrinsics, diversity, and randomness. A Causal Adversarial auto-Encoder (CAE) for SAR-ATR is then proposed to embody this disentangled representation, which incorporates a number of novel built-in network features. A physically reasonable Cyclic High-frequency information-based Embedding (CHE) method is proposed for azimuth encoding, which ensures the uniformity, continuity, periodicity, and distinctiveness of angle. A Symmetrically Conditional Encoding (SCE) module is established to constrain the semantic consistency of low-dimensional features. Besides, a hybrid loss function is designed, which is composed of latent adversarial loss, reconstruction loss, and task-oriented losses. Both representation and generalization abilities are thoroughly evaluated through qualitative visualization and quantitative comparison experiments on the MSTAR and FUSAR-Ship datasets. Experimental results demonstrate superior representation ability for the disentangled properties via angle-interpolation and target-transformation of SAR images. By using only 12 samples per-class, the proposed CAE can achieve an accuracy of 93.1% for the 10-target SAR-ATR classification task.