Comparison of radiated noise classification methods for underwater targets based on different enhanced images and convolutional neural networks

With the continuous development of economy and society, factors such as the variety of underwater targets and the high level of environmental noise have a great impact on the classification accuracy of underwater target radiation noise, and the traditional classification method based on signal features can no longer meet the requirements of underwater target identification. In this paper, we propose an underwater target radiation noise classification method based on enhanced image and convolutional neural network. First, the underwater target radiation noise signal is converted into enhanced image by various methods, then the converted image data set is used as the input of convolutional neural network for model training, and finally the great advantage of convolutional neural network in image classification is used to accurately classify underwater target radiation noise. In order to propose an optimal augmented image transformation method, this paper uses several augmented image transformation methods and compares the classification results. The experimental results show that the augmented image and convolutional neural network methods based on lagomorphs and corner fields have the highest classification accuracy and the best classification efficiency.