Underwater target classification at greater depths using deep neural network with joint multiple-domain feature

For underwater target classification which is supposed to recognise different ships with the radiated acoustic signal, it is the most challenging task to provide excellent classification accuracy in a variety of environments. However, most of the existing systems are optimised to get the best performance on the data set from certain situations which they are trained in, which may lead to generalisation risks when applied to new environments. Here, the authors introduce an underwater target classification framework using a deep neural network to learn deep features from a large joint multiple-domain input. The authors propose to incorporate spectral and wavelet domain information with different resolutions to grasp the 'global' structure and the 'local' transient variation of the raw radiated signals. In contrast to shallow models, a stacked sparse autoencoder (SSAE) model, which is composed of multiple hidden layers and a softmax classifier, is adopted to learn more discriminating features for classification. In the authors' experiments, the proposed SSAE model is evaluated on the data set consisting of underwater acoustic signal received at different ocean depths. The authors' results show that the proposed SSAE model with joint input features achieved a 5% improvement in classification accuracy compared to the state-of-the-art DBN approach.