Underwater sonar image classification using adaptive weights convolutional neural network

摘要

As an important part of oceanographic surveying, underwater sonar image classification has attracted much attention. Most of the existing classification methods cannot be widely used in underwater sonar image classification. However, deep learning models can automatically extract underwater sonar image features to improve the classification accuracy through an internal network structure. in the present study, a novel deep learning model with adaptive weights convolutional neural network (AW-CNN) was proposed to classify underwater sonar images. To solve the random initialization of filter weights in a convolutional neural network (CNN), the generated weights of the deep belief network (DBN) were applied to adaptively replace the randomly trained filter weights of the CNN in the implementation process of the AW-CNN for underwater sonar image classification. Specifically, first, dimension conversion was accomplished by using the increment-dimension function to unify the inputs of the CNN and the DBN. Then, according to the dimension conversion, the internal fusion of the two models is realized, and replacement of the randomly trained filter weights of the CNN was completed. Finally, in order to further improve the classification accuracy, the local response normalization (LRN) function is proposed to normalize the adaptive weights in the network initialization. Compared with other models, the classification results demonstrate that the proposed AW-CNN approach has the capability to effectively and successfully divide sonar images into their relevant seabed classes, which is beneficial to finding mines and shoals and detecting the integrated degree of the dam bottom. (C) 2018 Elsevier Ltd. All rights reserved.