Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

Abstract

Acoustic seabed classification (ASC) is a fast and large-scale seabed sediment survey method. In particular, combining it with an automated classifier can theoretically achieve fast automatic seabed sediment classification. However, owing to the cost of sampling, a lack of labeled data for sediment classification based on seabed acoustic images impedes the training and deployment of classifiers. Herein, we use shallow-water, side-scan sonar images collected from the Pearl River Estuary combined with deep learning to study sediment classification and optimization methods for a small dataset of seabed acoustic images. In this paper, we applied different and deeper convolutional neural networks (CNNs) and used grayscale CIFAR-10 for pretraining to achieve large-span parameter migration and improve model performance. The best result in the experiment is a 3.459% error rate achieved by ResNet after fine tuning, verifying the improvement brought by our fine tuning strategy and the deeper models used in such tasks. The results of data enhancement based on generative adversarial networks (GANs) indicated that this method can improve the accuracy of sediment classification; however, the effects of GANs are limited and they are computationally expensive. Overall, our findings resolve, to an extent, the dilemma of using small datasets of seabed acoustic images for sediment classification and provide a framework for future studies on sediment classification, which has a certain significance in helping people better understand the seabed.