Abstract:To solve the problem of small underwater objects classification based on multiview sonar images, a deep neural network classification method with multiview is proposed. Firstly, the shadow area of underwater objects in sonar images is extracted. The main axis slope of shadow area is calculated, which is used to match sonar images to the corresponding simulated dataset. The convolutional neural network trained by this simulated dataset is applied to extract deep neural network features from multiview sonar images. The achieved feature vectors from sonar images of different views are combined as a feature vector of underwater object and predicted from support vector machine. The classifier is utilized to classify multiview sonar images collected from lake and sea trials. The average classification accuracy can reach 9333%. The performance is improved compared with the single-view classification method using convolutional neural network and support vector machine.