Sonar data classification by using few-shot learning and concept extraction

Abstract

Given the heterogeneity and difficulty of classifying sonar targets, classifying these targets is a challenging problem in practice. In this paper, we propose a novel classification of sonar targets based on few-shot learning (FSL). The FSL can classify the data with only a few labeled training data. We also use the wavelet transform to denoise sonar signals and apply the short-time Fourier transform (STFT) to denoised signals. We propose a concept extraction method based on the STFT and find concept points to improve the performance of FSL classification. We evaluate the impact of concept numbers on accuracy. The main distinguishing feature of our proposed technique is the low sonar data requirement compared to other classification techniques. Our numerical experiments show that the proposed classification technique using the concept extraction method significantly improves the system performance in terms of accuracy.