Small-Sample Image Classification for Synthetic Aperture Sonar Based on Super-Resolution Reconstruction and Improved Self-Supervised Contrastive Learning

Synthetic aperture sonar (SAS) is the main means of underwater long-range and high-resolution imaging, and small-sample classification based on SAS images holds important application value. However, due to the constraints of sonar imaging mechanism, acquisition cost, and other factors, SAS images face the problems of insufficient target pixels and scarce sample size, which limit the performance of existing target classification methods. In this article, super-resolution reconstruction and contrastive learning mechanism are used to achieve highly accurate classification of SAS images with small samples. Considering the spatial distribution characteristics of fuzzy images, the super-resolution reconstruction algorithm is introduced to realize the pixel expansion and structural complementation of blurry images. To address the problem of scarce samples, a small-sample classification mechanism based on contrastive learning is innovatively designed, achieving high-accuracy classification of small-sample sonar images post super-resolution reconstruction. The results of the classification experiments on the SCTD dataset show that the proposed method can improve the accuracy by 4.17% compared with the classical classification method based on a convolutional neural network (CNN). The proposed method can not only serve to improve the performance of existing small-sample sonar images classification methods, but also illustrate the applicability of contrastive learning in the field of sonar image classification.