Underwater acoustic target recognition is a challenging task owing to the
intricate underwater environments and limited data availability. Insufficient
data can hinder the ability of recognition systems to support complex modeling,
thus impeding their advancement. To improve the generalization capacity of
recognition models, techniques such as data augmentation have been employed to
simulate underwater signals and diversify data distribution. However, the
complexity of underwater environments can cause the simulated signals to
deviate from real scenarios, resulting in biased models that are misguided by
non-true data. In this study, we propose two strategies to enhance the
generalization ability of models in the case of limited data while avoiding the
risk of performance degradation. First, as an alternative to traditional data
augmentation, we utilize smoothness-inducing regularization, which only
incorporates simulated signals in the regularization term. Additionally, we
propose a specialized spectrogram-based data augmentation strategy, namely
local masking and replicating (LMR), to capture inter-class relationships. Our
experiments and visualization analysis demonstrate the superiority of our
proposed strategies.

本研究提出在数据量有限的情况下，采用平滑性正则化，即只在正则项中加入模拟信号，并使用专门的光谱图基础数据增强策略（LMR），以避免性能下降的风险来增强模型的泛化能力。实验证明了该方法具有优越性。