  This paper proposes rollable latent space (RLS) for an azimuth invariant
synthetic aperture radar (SAR) target recognition. Scarce labeled data and
limited viewing direction are critical issues in SAR target recognition.The RLS
is a designed space in which rolling of latent features corresponds to 3D
rotation of an object. Thus latent features of an arbitrary view can be
inferred using those of different views. This characteristic further enables us
to augment data from limited viewing in RLS. RLS-based classifiers with and
without data augmentation and a conventional classifier trained with target
front shots are evaluated over untrained target back shots. Results show that
the RLS-based classifier with augmentation improves an accuracy by 30% compared
to the conventional classifier.
