Underwater Sonar Image Classification Using CWGAN-GP&DR And Improved Convolutional Neural Network

This study presents a generative adversarial network (GAN) called conditional Wasserstein GAN-gradient penalty (CWGAN-GP)&DenseNet and ResNet, and a convolutional neural network (CNN) called improved CNN to complete underwater sonar image classification. Specifically, to solve the problem of insufficient underwater sonar image data, the CWGAN-GP&DR is developed to expand underwater sonar image data set. Besides, to improve the analysis and utilisation of the feature map and reduce the misclassification rate of categories with similar probabilities, improved CNN is proposed to complete the final underwater sonar image classification. Finally, compared with other methods, the CWGAN-GP&DR generate better underwater sonar images and effectively expand the underwater sonar image data set. Moreover, compared with the original data set and other expanded data set, the highest accuracy rate of 85.00% can be obtained on the CWGAN-GP&DR expanded data set by CNN. Furthermore, CNN, CNN-bais and improved CNN are used to perform classification experiments on each data set, and the accuracy of the improved CNN is the highest on all data sets and reached the highest accuracy of 87.71% on CWGAN-GP&DR expanded data set. The experimental results demonstrate that the proposed method can effectively improve the performance of underwater sonar image classification.