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Underwater Sonar Image Classification with Small Samples Based on Parameter-based Transfer Learning and Deep Learning

The research of underwater sonar image classification is helpful to the exploration and development of marine resources. However, there are relatively few studies on the classification of underwater sonar image with small samples. Aiming at the problem, a new classification method of sonar image is proposed based on parameter-based transfer learning and deep learning (named SonarNet). Firstly, a new classification network of sonar image (Global Average Pooling VGGNet, GAPVGG) is designed based on VGG16 network, and GAPVGG can solve the over-fitting problem caused by training VGG16 network directly with small sample sonar image. Compared with the original VGG16 network, the number of parameters of the GAPVGG network is reduced by nearly 90%. Then, according to the parameter-based transfer learning, the knowledge is transferred from the source domain to the target domain to solve the problem of insufficient sonar image samples. Finally, it is demonstrated from experiments that the proposed method is effective, and the classification accuracy of sonar image reaches 98.25%.