Surface and underwater target classification under limited sample sizes based on sound field elevation structure

Abstract Surface/underwater target classification is a key topic in marine information research. However, the complex underwater environment, coupled with the diversity of target types and their variable characteristics, presents significant challenges for classifier design. For shallow-water waveguides with a negative thermocline, a residual neural network (ResNet) model based on the sound field elevation structure is constructed. This model demonstrates robust classification performance even when facing low signal-to-noise ratios and environmental mismatches. Meanwhile, to address the reduced generalization ability caused by limited labeled acoustic data, an improved ResNet model based on unsupervised domain adaptation (“proposed UDA-ResNet”) is further constructed. This model incorporates data on simulated elevation structures of the sound field to augment the training process. Adversarial training is employed to extract domain-invariant features from simulated and trial data. These strategies help reduce the negative impact caused by domain differences. Experimental results demonstrate that the proposed method shows strong surface/underwater target classification ability under limited sample sizes, thus confirming its feasibility and effectiveness.