SAR Image Classification Using Few-shot Cross-domain Transfer Learning

Data-driven classification algorithms based on deep convolutional neural networks have reached human-level performance for many tasks within Electro-Optical (EO) computer vision. Despite being the prevailing visual sensory data, EO imaging is not effective in applications such as environmental monitoring at extended periods, where data collection at occluded weather is necessary. Synthetic Aperture Radar (SAR) is an effective imaging tool to circumvent these limitations and collect visual sensory information continually. However, replicating the success of deep learning on SAR domains is not straightforward. This is mainly because training deep networks requires huge labeled datasets and data labeling is a lot more challenging in SAR domains. We develop an algorithm to transfer knowledge from EO domains to SAR domains to eliminate the need for huge labeled data points in the SAR domains. Our idea is to learn a shared domain-invariant embedding for cross-domain knowledge transfer such that the embedding is discriminative for two related EO and SAR tasks, while the latent data distributions for both domains remain similar. As a result, a classifier learned using mostly EO data can generalize well on the related task for the SAR domain.