  Synthetic Aperture Radar (SAR) imagery has diverse applications in land and
marine surveillance. Unlike electro-optical (EO) systems, these systems are not
affected by weather conditions and can be used in the day and night times. With
the growing importance of SAR imagery, it would be desirable if models trained
on widely available EO datasets can also be used for SAR images. In this work,
we consider transfer learning to leverage deep features from a network trained
on an EO ships dataset and generate predictions on SAR imagery. Furthermore, by
exploring the network activations in the form of class-activation maps (CAMs),
we visualize the transfer learning process to SAR imagery and gain insight on
how a deep network interprets a new modality.
