The utilization of deep learning algorithms for side-scan sonar target detection is impeded by the restricted quantity and representativeness of side-scan sonar (SSS) samples. To address this issue, this paper proposes a method for image augmentation using a CC-WGAN network. First, the generator incorporates the Convolutional Block Attention Module (CBAM) to enhance the assimilation of global information and local features in the input images. This integration also improves stability and avoids mode collapse problems associated with the original Generative Adversarial Network. Subsequently, the CBAM is incorporated into the discriminator to facilitate a better understanding of the relevance and significance of input data, thereby enhancing the model’s generalization ability. Finally, based on this model, existing few-sample SSS images are augmented, and we utilize the augmented images for discrimination and detection with YOLOv5. The experimental results show that following training with the SSS dataset that is augmented by this network, the accuracy of target detection increased by 7.6%, validating the feasibility of our proposed method. This method presents a novel solution to the problem of low model accuracy in underwater target detection with side-scan sonar due to limited samples.

Sonar is a valuable tool for ocean exploration since it can obtain a wealth of data. With the development of intelligent technology, deep learning has brought new vitality to underwater sonar image classification. However, due to the difficulty and high cost of acquiring underwater sonar images, we have to consider the extreme case when there are no available sonar data of a specific category, and how to improve the prediction ability of intelligent classification models for unseen sonar data. In this work, we design an underwater sonar image classification method based on Image Disentanglement Reconstruction and Zero-Shot Learning (IDR-ZSL). Initially, an image disentanglement reconstruction (IDR) network is proposed for generating pseudo-sonar samples. The IDR consists of two encoders, a decoder, and three discriminators. The first encoder is responsible for extracting the structure vectors of the optical images and the texture vectors of the sonar images; the decoder is in charge of combining the above vectors to generate the pseudo-sonar images; and the second encoder is in charge of disentangling the pseudo-sonar images. Furthermore, three discriminators are incorporated to determine the realness and texture quality of the reconstructed image and feedback to the decoder. Subsequently, the underwater sonar image classification model performs zero-shot learning based on the generated pseudo-sonar images. Experimental results show that IDR-ZSL can generate high-quality pseudo-sonar images, and improve the prediction accuracy of the zero-shot classifier on unseen classes of sonar images.

Underwater target detection is pivotal for marine exploration, yet it faces significant challenges because of the inherent complex underwater environment. Sonar images are generally degraded by noise, exhibit low resolution, and lack prominent target features, making the extraction of useful feature information from blurred and complex backgrounds particularly challenging. These limitations hinder highly accurate autonomous target detection in sonar imagery. To address these issues, this paper proposes the ABFP-YOLO model, which was designed to enhance the accuracy of underwater target detection. Specifically, the bi-directional feature pyramid network (BiFPN) structure is integrated into the model to efficiently fuse the features of different scales, significantly improving the capability of the network to recognize targets of varying scales, especially small targets in complex scenarios. Additionally, an attention module is incorporated to enhance feature extraction from blurred images, thereby boosting the detection accuracy of the model. To validate the proposed model’s effectiveness, extensive comparative and ablation experiments were conducted on two datasets. The experimental results demonstrate that the ABFP-YOLO model achieves mean average precision (mAP0.5) scores of 0.988 and 0.866, indicating its superior performance in target detection tasks within complex underwater environments.

The near-instantaneous radial velocity of a target can be obtained using the Doppler effect of SAR, which is widely used in ocean current retrieval. However, in sea ice drift velocity measurements, only a Doppler centroid estimation algorithm in frequency domain has been studied, so whether there is a better algorithm is worth exploring. In this study, based on Sentinel-1A IW data, three Doppler centroid estimation algorithms applied to ocean current retrieval are selected. Combined with the characteristics of the TOPS mode, made two applicability adjustments to each algorithm, and finally applied the three algorithms to sea ice radial surface velocity measurements. The first adjustment is to explore and determine the optimal parameters. The second adjustment is to use parallel computing to improve the efficiency, which is improved by an average of 43.55%. In addition, the deviation of Doppler centroid estimation bias correction is verified using rainforest data, and the deviation is controlled at approximately 3 Hz. Based on the three algorithms, five sets of experiments are carried out in this study. By analyzing and comparing the results of each algorithm, it is found that the results of the three algorithms are relatively consistent, among which the correlation Doppler estimation algorithm has the advantages of high efficiency and high precision and is the most suitable method for sea ice drift measurement among the three methods. However, for SAR images with abnormal speckles caused by human activities, the sign Doppler estimation algorithm can effectively remove abnormal speckles and ensure the smoothness of the image with better adaptability.

Sub-bottom profile data have the potential to characterize sediment properties but are seldom used for offshore site investigations because of uncertainties in rock-physics models. Deep-learning techniques appear to be poised to play very important roles in our processing flows for the interpretation of geophysical data. In this paper, a novel deep learning-based method for this task is proposed in which a nonlinear mapping between the observed data and sediment types is learned using a multi-attribute temporal convolution network (MATCN). Firstly, empirical mode decomposition (EMD) is employed for the original data, and intrinsic mode functions (IMFs) with multiple time scales are generated. Based on different IMFs, instantaneous frequency (IF) data under different IMFs can be obtained, while instantaneous phase (IP) and instantaneous amplitude (IA) data are obtained based on the original data. IF, IA and IP data are called attribute data, and are highly related to the attenuation, reflection, and interior structure of the sediment. Thus, IA, IF, and IP are used as the inputs, and a 1D convolutional neural network (CNN) and a time convolution network (TCN) are used to extract sequential features. Different feature representations are then fused. Combining cross-entropy loss function and class-edge loss function, the network is encouraged to produce classified results with more continuous sediment distributions compared with the traditional loss function. The real-data experiments demonstrate that the proposed MATCN has achieved good performance with an F measure greater than 70% in all cases, and greater than 80% in most cases.