Transfer Learning and Few-Shot Learning Based Deep Neural Network Models for Underwater Sonar Image Classification With a Few Samples

Abstract

Acoustic imaging sonar systems are widely used for long-range underwater surveillance in various civilian and military applications. They provide 2-D images of underwater objects, even in turbid water conditions where optical underwater imaging systems fail. Achieving high accuracy in automatic deep learning based underwater image classification remains an open problem due to insufficient data availability, poor image resolution, low signal-to-noise ratio surroundings, etc. In this study, we conduct a comparative analysis of different advanced deep learning approaches, i.e., transfer learning and few-shot learning, to address the problem of automatic object classification in sonar images, using a few samples of data. Specifically, two metric learning-based approaches, i.e., siamese network and triplet network as well as library-based approaches, are studied under the few-shot learning paradigm. Extensive experiments are conducted on a novel custom-made dataset developed in-house, along with the publicly available SeabedObjectsKLSG dataset. In addition, the effectiveness of the sampling technique in handling class imbalance during model training is also investigated in this work. Our experimental results highlight that the few-shot learning based approach is a promising direction for future research on underwater image classification with a few samples.