Search Results (46)

Classical beamforming (CBF) beamforming constrains the accuracy and quality of underwater acoustic imaging by producing wide main-lobes that reduce resolution, high sidelobes that cause leakage, and point-spread functions that blur targets. Existing approaches typically address only one of these issues at a time, limiting their ability to resolve multiple, interrelated problems simultaneously. In this study, we introduce a double-compression deconvolution high-resolution beamforming method designed to enhance multi-beam sonar imaging using an underwater uniform linear array. The proposed approach formulates imaging as a sparse deconvolution problem and suppresses off-target interference through two sparse constraints, thereby improving the sonar’s resolving capability. During sparse reconstruction, an auxiliary-parameter iterative shrinkage-threshold algorithm is employed to recover azimuthal sparse signals with higher accuracy. Simulations and controlled pool experiments demonstrate that, relative to classical beamforming, the proposed method significantly improves resolution, suppresses off-target interference, expands the imaging intensity dynamic range, and yields clearer target representations. This study provides an effective strategy to mitigate intrinsic limitations in high-resolution underwater sonar imaging. Full article

This study explores the integration of point cloud data obtained from unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) to address limitations in photogrammetry and to create comprehensive models of aquatic environments. The UAV platform (AUTEL EVO II) employs structure-from-motion (SfM) photogrammetry using optical imagery, while the USV (equipped with a NORBIT iWBMS multibeam sonar system) collects underwater bathymetric data. UAVs commonly face constraints in battery life and image-processing capacity, making it necessary to merge smaller UAV point clouds into larger, more complete models. The USV-derived bathymetric data are integrated with UAV-derived surface data to construct unified terrain models that include both above-water and underwater features. This study evaluates three coordinate transformation (CT) methods—4-parameter, 6-parameter, and 7-parameter—across three study areas in Taiwan to assess their effectiveness in registering USV–UAV and UAV–UAV point clouds. For USV–UAV integration, all CT methods improved alignment accuracy compared with results without CT, achieving decimeter-level precision. For UAV–UAV integrations, the 7-parameter method provided the best accuracy, especially in areas with low terrain roughness such as rooftops and pavements, while improvements were less pronounced in areas with high roughness such as tree canopies. These findings demonstrate that the 7-parameter CT method offers an effective and straightforward approach for accurate point cloud integration from different platforms and sensors. Full article

Offshore wind farms are rapidly expanding worldwide, and the submerged structures supporting wind turbines have the potential to function as artificial reefs for marine organisms. Quantitative visualization of fish aggregations around these foundations can provide valuable information for promoting collaboration between fisheries and offshore wind energy development. This study explored the use of multibeam sonar to detect spatial distributions and estimate the biomass of pelagic fish aggregations around the foundations of offshore wind power facilities. Fish distribution was extracted from multibeam water column image data using an automated sequence of filtering steps, ending with a spatial filter designed to remove common noise artifacts in multibeam sonar data. The resulting fish aggregations were visualized in three dimensions, revealing a tendency to cluster leeward of turbine and observation tower foundations, and fish biomass was successfully estimated from beam backscatter strength. The developed method can be applied to other offshore wind farms to demonstrate the role of turbine foundations as artificial reefs for fish. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

Artificial reefs serve as a crucial measure for preventing habitat degradation, enhancing primary productivity in marine areas, and restoring and increasing fishery resources, making them an essential component of marine ranching development. Accurate identification and detection of artificial reefs are vital for ecological conservation and fishery resource management. To achieve precise segmentation of artificial reefs in multibeam sonar images, this study proposes an improved YOLOv11-based model, YOLO-AR. Specifically, the DCCA (Dynamic Convolution Coordinate Attention) module is introduced into the backbone network to reduce the model’s sensitivity to complex seafloor environments. Additionally, a small-object detection layer is added to the neck network, along with the ultra-lightweight dynamic upsampling operator DySample (Dynamic Sampling), which enhances the model’s ability to segment small artificial reefs. Furthermore, some standard convolution layers in the backbone are replaced with ADown (Advanced Downsampling) to reduce the model’s complexity. Experimental results demonstrate that YOLO-AR achieves an mAP@0.5 of 0.912, an intersection-over-union (IOU) of 0.832, and an F1 score of 0.908. Meanwhile, the parameters and model size of YOLO-AR are 2.67 million and 5.58 MB. Compared to other advanced segmentation models, YOLO-AR maintains a more lightweight structure while delivering a superior segmentation performance. In real-world multibeam sonar images, YOLO-AR can accurately segment artificial reefs, making it highly effective for practical applications. Full article

While sonar sensors are crucial for underwater robotics perception, the key challenge lies in traditional multibeam sonar simulation’s lack of comprehensive physics-based interaction models. Such missing physical aspects lead to sonar imagery discrepancies, such as the absence of coherent imaging systems and speckle noise effects exposing risks of over-fitted control designs of the systems using the sonar perceptions. Previous research addressed this gap by introducing a physics-based simulation approach by direct calculation of the point-scattering model equations from perception data obtained from rasterization. However, the raster-based method could not control the resolution of data to pipeline into image generation, and its limitation was explicitly presented in local search scenarios where the distance between data is large. To eliminate those limitations and extend capabilities without losing the quality of the image, this paper introduces a ray-based approach to replace the raster-based method when obtaining the perception data from the simulated world to pipeline into physical equation calculations. The results of the ray-based and raster-based models are compared for the front floating object and the ground grazing local search scenario to confirm that the ray-based method maintains equal quality of sonar image generation, including physical characteristics, but it has more flexibility and capability in control of data resolution for correct sonar image generation. Full article

Side-scan sonar and multi-beam echo sounder (MBES) are the most widely used underwater surveying tools in marine mapping today. The MBES offers high accuracy in depth measurement but is limited by low imaging resolution due to beam density constraints. Conversely, side-scan sonar provides high-resolution backscatter intensity images but lacks precise positional information and often suffers from distortions. Thus, MBES and side-scan images complement each other in depth accuracy and imaging resolution. To obtain high-quality seafloor topography images in practice, matching between MBES and side-scan images is necessary. However, due to the significant differences in content and resolution between MBES depth images and side-scan backscatter images, they represent a typical example of heterogeneous images, making feature matching difficult with traditional image matching methods. To address this issue, this paper proposes a feature matching network based on the LoFTR algorithm, utilizing the intermediate layers of the ResNet-50 network to extract shared features between the two types of images. By leveraging self-attention and cross-attention mechanisms, the features of the MBES and side-scan images are combined, and a similarity matrix of the two modalities is calculated to achieve mutual matching. Experimental results show that, compared to traditional methods, the proposed model exhibits greater robustness to noise interference and effectively reduces noise. It also overcomes challenges, such as large nonlinear differences, significant geometric distortions, and high matching difficulty between the MBES and side-scan images, significantly improving the optimized image matching results. The matching error RMSE has been reduced to within six pixels, enabling the accurate matching of multi-beam and side-scan images. Full article

Marine geophysical and geological investigations are crucial for evaluating the construction suitability of artificial fish reefs (AFRs). Key factors such as seabed topography, geomorphology, sub-bottom structure, and sediment type significantly influence AFR design and site selection. Challenges such as material sinking, sediment instability, and scouring effects should be critically considered and addressed in the construction of AFR, particularly in areas with soft mud or dynamic environments. In this study, detailed investigations were conducted approximately seven months after the deployment of reef materials in the AFR experimental zones around Xiaoguan Island, located in the western South Yellow Sea, China. Based on morphological factors, using data from multibeam echosounders and side-scan sonar, the study area was divided into three geomorphic zones, namely, the tidal flat (TF), underwater erosion-accumulation slope (UEABS), and inclined erosion-accumulation shelf plain (IEASP) zones. The focus of this study was on the UEABS and IEASP experimental zones, where reef materials (concrete or stone blocks) were deployed seven months earlier. The comprehensive interpretation results of multi-source high-resolution acoustic images showed that the average settlement of individual reefs in the UEABS experimental zone was 0.49 m, and their surrounding seabed experienced little to no scouring. This suggested the formation of an effective range and height, making the zone suitable for AFR construction. However, in the IEASP experimental zone, the seabed sediment consisted of soft mud, causing the reef materials to sink into the seabed after deployment, preventing the formation of an effective range and height, and rendering the area unsuitable for AFR construction. These findings provided valuable scientific guidance for AFR construction in the study area and other similar coastal regions. Full article

Biogenic reefs support marine biodiversity and play a key role in a healthy marine environment. Protecting and enhancing reef-building species, such as Sabellaria spinulosa, require mapping and monitoring strategies. A multi-scale and multi-sensor mapping campaign, including a multi-beam echosounder, side-scan sonar (SSS), box corer and ROV with an attached video camera, has been carried out in the northern Brown Bank (Dutch Continental Shelf) in August 2023. A semi-automated classification workflow, based on a support vector machine (machine learning), was developed to map Sabellaria reefs using SSS and video data. Elevated Sabellaria reefs were classified with a precision and sensitivity of 52% and 49%, respectively. The classified SSS images were merged into full-coverage percentage maps of Sabellaria reef coverage. Located between the swales of the tidal ridges, it was estimated that the reefs cover an area of 3.8 to 5.7% within the surveyed areas. The maps indicate (1) on the large-scale a preference of Sabellaria spinulosa for settlement to the east of the deepest part of the swale and (2) on the small-scale a preference for the troughs towards the stoss side of the megaripples. The employed survey strategy and the developed classification workflow can be extended to other environmental areas and further developed into a standard monitoring procedure. Full article

This paper addresses the challenges of underwater Simultaneous Localization and Mapping (SLAM) using multibeam sonar imaging. The widely used Iterative Closest Point (ICP) often falls into local optima due to non-convexity and the lack of features for correct registration. To overcome this, we propose a novel registration algorithm based on Gaussian clustering and Graph Matching with maximal cliques. The proposed approach enhances feature-matching accuracy and robustness in complex underwater environments. Inertial measurements and velocity estimates are also fused for global state estimation. Comprehensive tests in simulated and real-world underwater environments have demonstrated that the proposed registration method effectively addresses the issue of the ICP algorithm easily falling into local optima while also exhibiting excellent inter-frame registration performance and robustness. Full article

Multi-beam sonar imaging detection technology is increasingly becoming the mainstream technology in fields such as hydraulic safety inspection and underwater target detection due to its ability to generate clearer images under low-visibility conditions. However, during the multi-beam sonar detection process, issues such as low image resolution and blurred imaging edges lead to decreased target segmentation accuracy. Traditional filtering methods for echo signals cannot effectively solve these problems. To address these challenges, this paper introduces, for the first time, a multi-beam sonar dataset against the background of simulated crack detection for dam safety. This dataset included simulated cracks detected by multi-beam sonar from various angles. The width of the cracks ranged from 3 cm to 9 cm, and the length ranged from 0.2 m to 1.5 m. In addition, this paper proposes a BS-UNet semantic segmentation algorithm. The Swin-UNet model incorporates a dual-layer routing attention mechanism to enhance the accuracy of sonar image detail segmentation. Furthermore, an online convolutional reparameterization structure was added to the output end of the model to improve the model’s capability to represent image features. Comparisons of the BS-UNet model with commonly used semantic segmentation models on the multi-beam sonar dataset consistently demonstrated the BS-UNet model’s superior performance, as it improved semantic segmentation evaluation metrics such as Precision and IoU by around 0.03 compared to the Swin-UNet model. In conclusion, BS-UNet can effectively be applied in multi-beam sonar image segmentation tasks. Full article

Autonomous Underwater Vehicle (AUV)-based multibeam bathymetry, sub-bottom profiles, and side-scan sonar images were collected in 2009 and 2010 to map the geomorphic features along the axial zone of a canyon (referred to as C4) within the canyon system developed on the northern slope of the South China Sea. These data significantly improved the spatial resolution of acoustic data, leading to a better understanding of the sedimentary processes within the modern canyon system. The bathymetric data reveal that sections across the canyon axis exhibit either asymmetrical or symmetrical characteristics, which differ from the overall asymmetrical pattern of the entire canyon. This suggests that the overall asymmetrical pattern of the canyon is not primarily due to axial incision. Various morphological elements were identified along the canyon axis, including failure scars, undulating features, knickpoints, flat terraces, furrows, and mass transport deposits (MTDs). Landslides, predominantly located in the upper canyon, were formed after at least 5000 years BP. Between the beginning of the canyon and a water depth of approximately 1300 m, there are alternating flat terraces and knickpoints. The large knickpoints’ low slope gradients are likely formed by the presence of undulating features. The internal configurations of undulating features suggest that they are depositional structures rather than sediment deformation. The formation of small-scale furrows below a depth of 1200 m may be associated with occasional gravity flows down the canyon. It is suggested that the canyon was generally inactive during the Holocene but experienced sporadic processes of sediment erosion, transport, and re-deposition in the axial zone that were triggered by landslide events occasionally in the upper canyon. Full article

Multibeam echosounders, as the most commonly used bathymetric equipment, have been widely applied in acquiring seabed topography and underwater sonar images. However, when interference occurs in the water column, traditional bottom detection methods may fail, resulting in discontinuities in the bathymetry and distortion in the sonar images. To solve this problem, we propose an anti-interference bottom detection method based on deep learning models. First, the variation differences of backscatter strengths at different incidence angles and the failure conditions of traditional methods were analyzed. Second, the details of our deep learning models are explained. And these models were trained using samples in the specular reflection, scatter reflection, and high-incidence angle regions, respectively. Third, the bottom detection procedures of the along-track and across-track water column data using the trained models are provided. In the experiments, multibeam data with strong interferences in the water column were selected. The bottom detection results of the along-track water column data at incidence angles of 0°, 35°, and 60° and the across-track ping data validated the effectiveness of our method. By comparison, our method acquired the correct bottom position when the traditional methods had inaccurate or even no detection results. Our method can be used to supplement existing methods and effectively improve bathymetry robustness under interference conditions. Full article

With rising interest in marine renewable energy (MRE) associated with offshore wind, waves, and tidal flows, the effects of device placement on changes in animal behaviour require proper assessment to minimise environmental impacts and inform decision making. High-frequency multibeam echosounders, or imaging sonars, can be used to observe and record the underwater movement and behaviour of animals at a fine scale (tens of metres). However, robust target detection and tracking of closely spaced animals are required for assessing animal–device and predator–prey interactions. Dual-frequency multibeam echosounders combine longer detection ranges (low frequency) with greater detail (high frequency) while maintaining a wide field of view and a full water column range compared to acoustic or optical cameras. This study evaluates the performance of the Tritech Gemini 1200ik imaging sonar at 720 kHz (low frequency) and 1200 kHz (high frequency) for small target detection with increasing range and the ability of the two frequency modes to discriminate between two closely spaced targets using a 38.1 mm tungsten carbide acoustic calibration sphere under controlled conditions. The quality of target detection decreases for both modes with increasing range, with a 25 m limit of detection at high frequency and a low-frequency mode able to detect the target up to 30 m under test conditions in shallow water. We quantified the enhanced performance of the high-frequency mode in discriminating targets at short ranges and improved target detection and discrimination at high ranges in the low-frequency mode. Full article

The upper part of the Krka River estuary and Prokljan Lake are a specific example of a well-stratified estuarine environment in a submerged river canyon. Here, we reconstructed the geomorphological evolution of the area and classified the data gathered in the study, integrating multibeam echosounder data, backscatter echosounder data, side-scan sonar morpho-bathymetric surveys, and acoustic sub-bottom profiling, with the addition of ground-truthing and sediment analyses. This led to the successful classification of the bottom sediments using the object-based image analysis method. Additional inputs to the multibeam echosounder data improved the segmentation of the seafloor classification, geology, and morphology of the surveyed area. This study uncovered and precisely defined distinct geomorphological features, specifically submerged tufa barriers and carbonate mounds active during the Holocene warm periods, analogous to recent tufa barriers that still exist and grow in the upstream part of the Krka River. Fine-grained sediments, classified as estuarine sediments, hold more organic carbon than coarse-grained sediments sampled on barriers. A good correlation of organic carbon with silt sediments allowed the construction of a prediction map for marine sedimentary carbon in this estuarine/lake environment using multibeam echosounder data. Our findings highlight the importance of additional inputs to multibeam echosounder data to achieve the most accurate results. Full article