  This letter presents a novel framework termed DistSTN for the task of
synthetic aperture radar (SAR) automatic target recognition (ATR). In contrast
to the conventional SAR ATR algorithms, DistSTN considers a more challenging
practical scenario for non-cooperative targets whose aspect angles for training
are incomplete and limited in a partial range while those of testing samples
are unlimited. To address this issue, instead of learning the pose invariant
features, DistSTN newly involves an elaborated feature disentangling model to
separate the learned pose factors of a SAR target from the identity ones so
that they can independently control the representation process of the target
image. To disentangle the explainable pose factors, we develop a pose
discrepancy spatial transformer module in DistSTN to characterize the intrinsic
transformation between the factors of two different targets with an explicit
geometric model. Furthermore, DistSTN develops an amortized inference scheme
that enables efficient feature extraction and recognition using an
encoder-decoder mechanism. Experimental results with the moving and stationary
target acquisition and recognition (MSTAR) benchmark demonstrate the
effectiveness of our proposed approach. Compared with the other ATR algorithms,
DistSTN can achieve higher recognition accuracy.
