Rollable Latent Space for Azimuth Invariant SAR Target Recognition
This addresses azimuth invariance in SAR target recognition, which is a domain-specific challenge for radar imaging applications, but the approach appears incremental as it builds on latent space manipulation techniques.
The paper tackles the problem of synthetic aperture radar (SAR) target recognition with scarce labeled data and limited viewing directions by proposing a rollable latent space (RLS) that enables data augmentation and improves accuracy by 30% compared to conventional methods.
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.