Unlabeled Samples Improve Few-Shot Underwater Acoustic Target Recognition

The complex underwater environments pose major challenges to acoustic target recognition with insufficient labeled samples and mismatched domain issues. Although current few-shot learning (FSL) methods could alleviate the data scarcity problem, yet they still suffer from limited application scenarios and poor performance. Here, this article proposes a novel domain-adapted few-shot underwater acoustic target recognition (UATR) method based on environmental feature adaptation and frequency feature contrast (EFFC) that fully leverages unlabeled samples in the target domain to enhance model fine-tuning performance. First, an environmental feature adaptation module is purposely designed to integrate the weighted statistics of unlabeled samples from the current domain to capture the environmental information, promoting rapid adaptation to new environments. Next, a frequency feature contrast module is specifically devised by utilizing contrastive-learning to understand the characteristics of frequency bands from augmented, unlabeled samples, enabling the model to learn the general feature representations of ship-radiated noise. The performance is comprehensively evaluated on three public datasets, ShipsEar, DeepShip, and QiandaoEar22, as well as our dataset, DanShip, which is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NPU-416/DanShip</uri>. Experimental results demonstrate that the model achieves the highest accuracy rates of 72.91%, 85.67%, 98.67%, and 82.56% on the ShipsEar, DeepShip, QiandaoEar22, and DanShip datasets, respectively, outperforming the other FSL methods.