Marine Moving Target Classification Based on Capsule Network with Feature Enhancement

The classification of marine moving targets is an important research topic. There are many methods to apply deep learning to target classification, such as convolution neural networks (CNNs). However, CNNs have complex network structures and large numbers of parameters, and cannot pay attention to the spatial information of the target. This paper proposes a new method for the classification of marine moving targets based on an adaptive multi-feature extraction module and a capsule network (DA_CapsNet). The attention mechanism captures the dependencies between feature channels and enhances the weights of salient features. Different from the Squeeze -and -Excitation network (SENet), SENet_avgmax can weight the feature maps comprehensively. Experimental results show that the proposed method achieves better classification performance compared to the state-of-theart methods.