Small-Sample Seabed Sediment Classification Based on Deep Learning

Abstract

Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier. However, acquiring seabed sediment information is expensive and time-consuming, which makes it crucial to design a well-performing classifier using small-sample seabed sediment data. To avoid data shortage, a self-attention generative adversarial network (SAGAN) was trained for data augmentation in this study. SAGAN consists of a generator, which generates data similar to the real image, and a discriminator, which distinguishes whether the image is real or generated. Furthermore, a new classifier for seabed sediment based on self-attention densely connected convolutional network (SADenseNet) is proposed to improve the classification accuracy of seabed sediment. The SADenseNet was trained using augmented images to improve the classification performance. The self-attention mechanism can scan the global image to obtain global features of the sediment image and is able to highlight key regions, improving the efficiency and accuracy of visual information processing. The proposed SADenseNet trained with the augmented dataset had the best performance, with classification accuracies of 92.31%, 95.72%, 97.85%, and 95.28% for rock, sand, mud, and overall, respectively, with a kappa coefficient of 0.934. The twelve classifiers trained with the augmented dataset improved the classification accuracy by 2.25%, 5.12%, 0.97%, and 2.64% for rock, sand, mud, and overall, respectively, and the kappa coefficient by 0.041 compared to the original dataset. In this study, SAGAN can enrich the features of the data, which makes the trained classification networks have better generalization. Compared with the state-of-the-art classifiers, the proposed SADenseNet has better classification performance.

A back propagation neural network-based approach for inverting layered seabed acoustic parameters in shallow waters

“…Such as the base is covered by a layer of sedimentary layer. As a result of this time, the physical characteristics of the physical characteristics of the seabed and semi-infinite seabed have a large difference and cannot be used to solve a similar problem of the semi-infinite seabed (Zhao et al, 2023). And so the previous introduction of the stratified seabed model, due to the oceanic motion and crustal movement of a stochastic, and so the base covered by the number of sedimentary layers is also not given to a fixed number of layers, it needs to be reflected according to the characteristics of the signal (Zhu et al, 2023b).…”

Underwater Image Translation via Multi-Scale Generative Adversarial Network

“…Underwater images play critical roles in diverse marine-related military and scientific applications, such as seabed sediment classification [1], submarine cable detection [2], and mine recognition [3]. However, the complex underwater environment limits the use of camera devices, including Kinect units [4] and binocular stereo cameras [5], which makes it difficult to obtain real underwater images.…”