Boosting SONAR Image Classification Through Transfer Learning

In this work, we address the challenge of limited labeled training data in deep learning-based under-water SONAR applications. We highlight the importance of transfer learning as a solution to mitigate this issue. Our methodology involves pretraining a classification model on data from alternative sources, such as Synthetic Aperture Radar (SAR) imagery, and subsequently fine-tuning the model on a target underwater SONAR dataset. By trans-ferring the critical features learned during pretraining, our approach significantly enhances classification accuracy on the SONAR dataset. Experimental results confirm the effectiveness of this approach, demonstrating the potential of transfer learning to improve performance in underwater sensing and enable more accurate detection of underwater objects.