Data augmentation and deep neural network classification based on ship radiated noise

Abstract

IntroductionVarious types of ships sail at sea, and identifying maritime ship types through shipradiated noise is one of the tasks of ocean observation. The ocean environment is complex and changeable, such rapid environmental changes underline the difficulties of obtaining a huge amount of samples. Meanwhile, the length of each sample has a decisive influence on the classification results, but there is no universal sampling length selection standard.MethodsThis study proposes an effective framework for ship-radiated noise classification. The framework includes: i) A comprehensive judgment method based on multiple features for sample length selecting. ii) One-dimensional deep convolution generative adversarial network (1-DDCGAN) model to augment the training datasets for small sample problem. iii) One-dimensional convolution neural network (CNN) trained by generated data and real data for ship-radiated noise classification. On this basis, a onedimensional residual network (ResNet) is designed to improve classification accuracy.ResultsExperiments are performed to verify the proposed framework using public datasets. After data augmentation, statistical parameters are used to measure the similarity between the original samples and the generated samples. Then, the generated samples are integrated into the training set. The convergence speed of the network is clearly accelerated, and the classification accuracy is significantly improved in the one-dimensional CNN and ResNet.DiscussionIn this study, we propose an effective framework for the lack of scientific sample length selection and lack of sample number in the classification of ship-radiated noise, but there aret still some problems: high complexity, structural redundancy, poor adaptability, and so on. They are also long-standing problems in this field that needs to be solved urgently.