Transfer Learning with SAS-Image Convolutional Neural Networks for Improved Underwater Target Classification

The value of transferring convolutional neural networks (CNNs) trained with synthetic aperture sonar (SAS) imagery is demonstrated in the context of an underwater unexploded ordnance (UXO) classification task. Specifically, it is shown that CNNs designed for, and trained on, a mine classification task can be transferred across sensors of the same modality - but different frequency bands and sensor resolutions - and also across target concept (from mines to UXO). Importantly, it is shown that this transfer learning outperforms simply training the CNNs "from scratch" using the limited available data that pertains to the ultimate task. A key element underlying this approach is that the CNNs be specially tailored to the particularities of the sensor modality and its data. These findings are valuable because they illustrate how training-data requirements can be eased for data-limited remote-sensing applications.