To enable side-scan sonar (SSS) to be used for large-scale seabed sediment detection, classification, and composition analysis, this article breaks through the limitations of past superficial use of echo characteristics and proposes a hidden feature extraction method that combines imaging mechanisms with seabed reflection models. By using the penetration characteristics of sound waves, weak echo width (WEW) features were extracted from the water column region of SSS echoes. By using the Lambertian model and the concept of denoising diffusion probabilistic models (DDPMs), seabed topography and sediment reflection features were, furthermore, successfully decomposed from SSS echoes, and a method to extract features highly correlated with sediment composition from these components is also presented. The effectiveness of these hidden features was demonstrated through sediment classification and analysis experiments in the Bohai Bay of China. Compared to using only echo features, the hidden features extracted in this article improved classification accuracy by over 20% on common classifiers. Based on the hidden features and sediment distribution, the causes of seabed composition near Caofeidian were explained. Additionally, through feature importance and sediment response experiments, the response mechanisms of different hidden features to various sediment types were scientifically interpreted. This study demonstrates the significant application value of SSS in large-scale seabed sediment detection, classification, and analysis for geoscientific tasks.

To overcome the challenges of limited samples, difficult acquisition, under-representation, and labeling in utilizing sonar images and deep learning for target detection, recognition, and segmentation tasks for full-class underwater targets, we propose the Seg2Sonar network based on SPADE. This network generates images through segmentation maps, thus eliminating the need for sample annotation. Additionally, we incorporate the Skip-Layer channel-wise Excitation (SLE) module into the SPADE network to enhance feature extraction ability with minimal training samples. To improve the realism of generated images, we introduce the Focal Frequency Loss (FFL) module, and propose the Elasticity loss (EL) strategy to improve the random combination capability of the network, considering the characteristics of low resolution and severe distortion of sonar images. Furthermore, we propose a weight adjustment (WA) strategy that tackles the challenge of low and unbalanced feature representation with few samples by taking into account the unbalanced distribution of features using prior information. hese four improvements enable efficient sample augmentation of sonar images with limited samples. Building upon the improved Seg2Sonar network, we propose an underwater full-class target augmentation strategy. Based on the imaging characteristics of sonar images, we classify underwater full-class targets into four categories: texture level, group level, shape level, and intensity level. We provide corresponding augmentation strategies by leveraging similar features among sonar target images or adding external radar/optical features to supplement the diversity of features. Our experimental results demonstrate the efficacy of our proposed method in achieving sample augmentation of underwater full-class targets with minimal samples (less than 10) or even zero samples. The approach achieves about 90% accuracy in detection, recognition, and segmentation for all types of targets through deep learning methods. Our findings provide a promising solution for efficient sample augmentation of underwater full-class targets with limited samples.

To meet the demands of marine scientific research and ocean engineering construction for cost-effective, high-resolution, and large-scale acquisition of seafloor topography, this study proposes an unsupervised terrain reconstruction technique for side-scan sonar (SSS). Unlike conventional methods, this technique does not rely on externally measured depths for initial terrain and ground truth; instead, it constructs these elements based on its own imaging mechanism. Through the development of a rigorous seafloor reflection model, we have designed an unsupervised depth convolutional neural network (DCNN) inversion framework. This framework utilizes the depth of the sea bottom line as the initial terrain and employs the height calculated from the target’s shadow as the ground truth. The proposed network not only extracts absolute topographical information but also captures relative changes in beam pattern and seabed substrate properties. The experimental results demonstrate the method’s excellent accuracy in terrain inversion for areas with water depths ranging from 10 to 14 m, achieving a vertical accuracy exceeding 0.17 m. Moreover, the method exhibits robust resilience to residual radiometric distortions, variations in seabed substrate, noise, and shadows. This innovative approach provides a reliable methodology for acquiring and broadly applying high-resolution seafloor terrain, potentially advancing various fields of marine research and engineering.

To address the drawbacks that current multibeam bathymetric outlier removal methods lack repeatability, often require parameter adjustment for different regions, still require a lot of manual labor, and offer limited scalability, a deep learning-based outlier removal method for multibeam bathymetric data is proposed. The method fully considers the multibeam data measurement principles and causes of outliers in multibeam bathymetric data and includes a comprehensive sample augmentation method and an outlier removal model based on a modification of a recently proposed PCPNet architecture. In our extensive evaluation, both on synthetic and real data, our method demonstrates robust outlier removal performance in a variety of marine environments without any parameter adjustment.

Air bubbles in seawater always lead to information loss for sub-bottom profiler (SBP) images. This common issue makes it difficult to interpret SBP data. Toward this end, this letter proposes a hybrid method to reconstruct the missing area on an SBP image. This method combines the information of multi-survey line to overcome the disadvantage of using a single survey line that has less observation information. First, based on an improved active contour model (ACM) algorithm and a bottom line match algorithm, the SBP images of adjacent survey lines are aligned, and the image patch group is obtained. Then, a multi-survey line patch group deep learning framework is trained to reconstruct the missing area using the patch group as the input layer. The global–local loss function is employed to optimize the deep learning training model. Both simulated and real experiments have been used to test the effectiveness of the proposed method, and good results have been achieved.

Reconstructing high-resolution underwater topography using side scan sonar (SSS) images with self-constraint may fail at the marginal part of the surveying area. To solve this problem, the bathymetric data are integrated as the reference to obtain high-resolution and high-accuracy topography. First, based on the seafloor reflection model, the SSS-based reconstructed terrain is obtained. Then, the integration model of SSS-based reconstructed results and bathymetric data in the same surveying area is proposed. Experiments have been used to test the effectiveness of the proposed method and good results have been achieved.

To solve the shortage of training samples when using deep learning to detect shipwrecks, a comprehensive sample augmentation method is proposed. The method fully considers the imaging mechanism and environment of side-scan sonar (SSS), such as acoustic emission and reception, waterbody, target reflection, and seafloor background, and generates diverse and representative shipwreck samples from five aspects of target diversity, target texture, imaging resolution, equipment and environmental noises, and background through a series of novel sample augmentation methods. Under the condition of zero real SSS samples, a detection model of YOLOv5s was established with these amplified samples and achieved a mean average precision (MAP) better than 96% for real SSS data detection.

Nowadays, a 3-D sub-bottom profiler (SBP) can produce the point clouds of the subseabed and is gradually receiving more attention in getting geologically significant surfaces to reveal sedimentary environments and structural features. However, little literature studied the automatic extraction of these surfaces from the 3-D SBP data currently. Thus, this article proposes a hybrid method consisting of a surface extraction algorithm and a segmentation algorithm. First, the multiprofile SBP data are converted into 3-D data volume. Second, by taking full advantage of the plate-like characteristic of the layer surface in the 3-D SBP data, a plate-like enhancement filtering algorithm based on the nonuniform Gaussian scale is given to filter the 3-D data volume. Third, a threshold extraction is applied to extract surface voxels, and a hybrid region growing algorithm is put forward to segment surface voxels into basic units by combining multicriteria. Finally, the surface segmentation problem is formulated as global energy optimization, and a stepwise segmentation algorithm is proposed to get the final surface set. To verify the effectiveness of the proposed method, experiments were conducted and analyzed. The results showed that the proposed method performed well.

The towing operation mode of side‐scan sonar system (SSS) easily results in dislocations and distortions of targets in the SSS strip image, brings difficulty to the mosaic of these strip images by the geocoding method or the tessellation‐line method and affects the recognition and understanding for seabed relief. Therefore this study proposes a new method, namely the segment‐image mosaic method based on corresponding features of the two adjacent SSS strip images. Through SSS image preprocessing, segment match based on corresponding features and image fusion in the common coverage area based on wavelet transformation, this new method overcomes the drawbacks of the traditional methods of image mosaic, fulfils well the mosaic of SSS strip images and finally a whole‐area SSS image is formed. Experiments have verified that the mosaic image formed by the new method can correctly reflect the position, shape and distribution of seabed targets, which is helpful to understand seabed relief.