In this paper, on the basis of the sparse weighted least-squares support vector machine (LS-SVM) algorithm, the sparse weighted LS-SVM surface is established and the corresponding steps are given. Then, multibeam bathymetric anomalies can be detected using the sparse weighted LS-SVM surface. The construction of the sparse weighted LS-SVM surface requires four aspects: selection of kernel function, parameters calculation of sparse weighted LS-SVM, selection of support vectors, calculation of weight coefficients. Finally, to verify the validity of sparse weighted LS-SVM surface in multibeam bathymetric anomalies detection, the measured multibeam bathymetric data are selected to calculate and analyze. The conclusion of the experiment is introduced, that is, the sparse weighted LS-SVM surface has a better performance compared to the traditional polynomial surface function, and the sparse weighted LS-SVM surface is more capable of reflecting the overall change of seabed topography. Compared with the combined uncertainty and bathymetry estimator surface, the proposed method utilizes the raw soundings rather than the nodes, so the sparse weighted LS-SVM surface can better reflect the detailed information of seabed topography.

Radiometric distortion caused by the time-varying gain (TVG), beam patterns, angular responses, and sonar altitude variations, highly degrades the quality of side-scan sonar (SSS) images. Thus, radiometric distortion correction becomes a fundamental step for SSS image processing which holds vital importance for geomorphic applications. However, existing methods cannot take the prior information of the acoustic illumination component as well as the feature of seafloor into consideration well, which would easily cause damage to the image and also always be powerless for residual stripe noise. In this paper, a novel radiometric correction method is proposed. First, we give a detailed analysis of the SSS imaging theory based on the Lambert’s law as well as the prior knowledge about the characteristics of SSS images. Then, incorporating the prior of the SSS imaging process, the low-rank constraint is specifically introduced for the illumination component, while the anisotropic total variation (ATV) constraint is used to constraint the albedo component, combining other constraints, a decomposition model is proposed to correct the radiometric distortion based on the SSS imaging theory. And an alternative minimization method has been adopted to solve the proposed model effectively. Experiments proved the validity of the proposed method.

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.

To understand the influence of sea ice on shipborne gravity measurements and the accuracy of the satellite-altimetry-derived gravity field in the Arctic Ocean, we compared shipborne gravity measurements with those obtained from satellite altimetric gravity measurements. The influence of sea ice on the shipborne gravity measurements was mainly concentrated in the 0–6 km wavelength range, and the standard deviation of the noise amplitudes was 2.62 mGal. Compared to ice-free regions, the accuracies in the region with floating ice were reduced by 13% for DTU21 and 6% for SV31. Due to the influence of sea ice, satellite altimetric gravity data lose significant information in the 9–12 km wavelength range. The coherence curve of the shipborne gravity with bathymetry was nearly the same as that of the satellite altimetric gravity. The satellite data contain nearly all of the significant information that is present in the shipborne data. The differences between the shipborne and satellite gravity data are small and can be used to study the crustal structure of the Arctic.

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.