Underwater objects classification method in high-resolution sonar images using deep neural network

To solve the problem of underwater proud object classification under small sample size situation where sonar image data is lacked,a classification method using deep neural network is proposed.Firstly,statistical characteristics of acoustic shadow regions are modeled using Gaussian mixture model and acoustic shadows are extracted.Trial and simulated dataset are constructed on this basis.Then,simulated dataset is input into convolutional neural network for training,and the feature extraction part is retained,which is used to extract feature of trial dataset.The classification part is reconstructed and trained by feature vectors of trial dataset.The experimental results show that the average classification accuracy of the proposed method is 88.24%,which is 8.67%,20.47%,19.78%,11.59%,9.01%,11.58%higher than that of other six contrast methods respectively.It verifies that the proposed method achieves better performance on underwater proud object classification problem.The learning curve converges to an accuracy of 96.25%,which is only 5.14% higher than the validation curve,indicating that the over-fitting problem is alleviated to some extent.Improved convolutional neural network is applied in a fusion classifier,which also combines the output of Logistic Regression classifier,support vector machine,and finally obtains a fusion result.The classification accuracy is up to 93.33%,indicating that fusion classifier improves the robustness and classification performance of the algorithm further.