Visualization of Deep Transfer Learning In SAR Imagery
This work addresses the challenge of adapting widely available EO-trained models for SAR imagery, which is useful for surveillance applications, but it is incremental as it applies existing transfer learning and visualization techniques to a new data type.
The paper tackled the problem of applying models trained on electro-optical (EO) datasets to synthetic aperture radar (SAR) imagery using transfer learning, and visualized the process with class-activation maps to understand how deep networks interpret the new modality.
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.