Side-scan Sonar Research Articles

Underwater Wireless Sensor Network-Based Delaunay Triangulation (UWSN-DT) Algorithm for Sonar Map Fusion

Abstract Robust and fast image recognition and matching is an important task in the underwater domain. The primary focus of this work is on extracting subsea features with sonar sensor for further Autonomous Underwater Vehicle navigation, such as the robotic localization and landmark mapping applications. With the assistance of high-resolution underwater features in the Side Scan Sonar (SSS) images, an efficient feature detector and descriptor, Speeded Up Robust Feature, is employed to seabed sonar image fusion task. In order to solve the nonlinear intensity difference problem in SSS images, the main novelty of this work is the proposed Underwater Wireless Sensor Network-based Delaunay Triangulation (UWSN-DT) algorithm for improving the performances of sonar map fusion accuracy with low computational complexity, in which the wireless nodes are considered as underwater feature points, since nodes could provide sufficiently useful information for the underwater map fusion, such as the location. In the simulated experiments, it shows that the presented UWSN-DT approach works efficiently and robustly, especially for the subsea environments where there are few distinguishable feature points.

Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets

This study introduces a novel approach to the critical task of submarine pipeline or cable (POC) detection by employing GoogleNet for the automatic recognition of side-scan sonar (SSS) images. The traditional interpretation methods, heavily reliant on human interpretation, are replaced with a more reliable deep-learning-based methodology. We explored the enhancement of model accuracy via transfer learning and scrutinized the influence of three distinct pre-training datasets on the model’s performance. The results indicate that GoogleNet facilitated effective identification, with accuracy and precision rates exceeding 90%. Furthermore, pre-training with the ImageNet dataset increased prediction accuracy by about 10% compared to the model without pre-training. The model’s prediction ability was best promoted by pre-training datasets in the following order: Marine-PULSE ≥ ImageNet > SeabedObjects-KLSG. Our study shows that pre-training dataset categories, dataset volume, and data consistency with predicted data are crucial factors affecting pre-training outcomes. These findings set the stage for future research on automatic pipeline detection using deep learning techniques and emphasize the significance of suitable pre-training dataset selection for CNN models.

Side scan sonar data quantification for seabed classification in Yos Sudarso Bay, Jayapura

Side Scan Sonar (SSS) is an instrument that is usually used in surveys to perform seabed imaging. SSS was able to show two-dimensional images of the seabed with contour, topographic, and target conditions simultaneously. This technology is capable of distinguishing seabed types such as rocks, sand, or others. In addition, the usefulness of this technology can also detect the existence of marine pipes and cables, shallow structures of the seabed, dredges, environmental studies, military, archaeology, fisheries, and mining. The aims of this research were to identify and interpret the seabed, measure the acoustic backscatter (dB) and amplitude (mV) of the seabed substrate, and compute SSS data using Continuous Wavelet Transform (CWT) in the Yos Sudarso Bay, Jayapura. Processing data was performed by radiometric Beam Angle Correction (BAC), Automatic Gain Control (AGC), and geometric (bottom tracking) corrections using SonarWiz 7 software. Pre-processed SSS data were then converted to XtfToSegy and the amplitude values were exported by SeiSee software, and CWT was calculated using Matlab. Amplitude data was visualized using Microsoft Excel to display a relation between amplitude, time, and CWT graph. The results of seabed detection were three areas of sand and one coral target. The amplitude value of sand 1 ranged from 4319 to 30976 mV with the = backscatter value was -17.10 dB. The amplitude value of sand 2 ranged from 1411 to 12288 mV with the backscatter value was -18.79 dB. The amplitude value of sand 3 ranged from 1391.625 to 12032 mV with the backscatter value was -18.70 dB. The amplitude of the coral ranged from 24960 to 32640 mV with the backscatter value was -2.30 dB. The CWT analysis results show that the yellow-coloured coral’s energy represents a rougher and harder object. The energy generated from the blue-coloured sand represents the object has smooth and non-hardening characteristics.

Bathymetry mapping for pioneer ship voyages using side scan sonar in Sembulang Waters, Riau Islands

The SSS C-MAX CM2 Tow fish instrument is an acoustic instrument that operates at a frequency of 325 kHz with a maximum sounding distance of 200 m towing cable length of 50 m, producing images and textures within a large area. This study aimed to process data from the SSS C-max CM2 measurement survey to obtain MSL and LLWL tidal corrected bathymetry data, which is used to create a sailing map for pioneer ships in Sembulang Waters, Riau Islands. The SSS data acquisition was carried out on March 15 - 18, 2021, in Sembulang Waters using a 5GT vessel with a Batam State Polytechnic team. Tidal data was taken using a tidal board placed at Sembulang Pier. SSS data processing used Sonar Wiz 7 licensed software obtained from Pushidrosal with the application of several geometric and radiometric corrections. Geometric corrections included layback, bottom tracking, and slant range correction. Radiometric correction consists of Beam angle Correction (BAC), Automatic Gain Control (AGC), Time-Varying Gain (TVG), and Empirical Gain Normalization (EGN). Tidal data was processed using the Admiralty method. SSS data was exported into .csv format for making topographic maps of the seabed based on the depth and width of the ideal shipping lane based on the specifications of traditional fishing vessels used at the research site. The actual depth value of the Sembulang Waters ranges from 0.5 meters to 27.7 meters. The depth of the waters towards the east is getting deeper. The depth value of the Sembulang Waters in LLWL conditions ranges from 0 meters to 26.5086 meters, shows that several locations need to be avoided for ships sailing area and need to be dredging for the safety of ships ≥ 5GT.

The Ability to Detect Sound Scattering Layers by Interferometric Side-Scan Sonar

The Ability to Detect Sound Scattering Layers by Interferometric Side-Scan Sonar

Small-Sample Underwater Target Detection: A Joint Approach Utilizing Diffusion and YOLOv7 Model

Underwater target detection technology plays a crucial role in the autonomous exploration of underwater vehicles. In recent years, significant progress has been made in the field of target detection through the application of artificial intelligence technology. Effectively applying AI techniques to underwater target detection is a highly promising area of research. However, the difficulty and high cost of underwater acoustic data collection have led to a severe lack of data, greatly restricting the development of deep-learning-based target detection methods. The present study is the first to utilize diffusion models for generating underwater acoustic data, thereby effectively addressing the issue of poor detection performance arising from the scarcity of underwater acoustic data. Firstly, we place iron cylinders and cones underwater (simulating small preset targets such as mines). Subsequently, we employ an autonomous underwater vehicle (AUV) equipped with side-scan sonar (SSS) to obtain underwater target data. The collected target data are augmented using the denoising diffusion probabilistic model (DDPM). Finally, the augmented data are used to train an improved YOLOv7 model, and its detection performance is evaluated on a test set. The results demonstrate the effectiveness of the proposed method in generating similar data and overcoming the challenge of limited training sample data. Compared to models trained solely on the original data, the model trained with augmented data shows a mean average precision (mAP) improvement of approximately 30% across various mainstream detection networks. Additionally, compared to the original model, the improved YOLOv7 model proposed in this study exhibits a 2% increase in mAP on the underwater dataset.

Using down-scan capabilities from recreational-grade side-scan sonar systems to sample paddlefish and evaluate depth use in a reservoir

Recreational-grade side-scan sonar (SSS) has only recently been applied to estimate abundance of Paddlefish Polyodon spathula, a large pelagic planktivore, in reservoirs. Current recreational-grade SSS units also have a dedicated down-scan channel, which may be useful for detecting Paddlefish in reservoirs because the range of depths they inhabit. We investigated the utility of down-scan images using SSS data from a previously published study of Paddlefish in Keystone Lake, Oklahoma. Two readers counted Paddlefish and estimated the depth of each fish and the water column. We used proximity functions in a geographic information system to find individual Paddlefish that were common between the readers. We further used proximity functions to identify common fish observed from the SSS survey conducted previously as an aid to compare and refine down-scan estimates. Depth of Paddlefish averaged approximately 7 m, but fish were deeper when water was deeper. Density estimates from down-scan were comparable to side-scan, but only after utilizing the side-scan data to adjust for detection-by-distance in a dual-gear approach. Down-scan data thus appear to be useful for not only estimating density of Paddlefish, but also for incorporating depth use, creating a three-dimensional database of locations that can inform managers of optimal depths for sampling gear (e.g., gill nets), improve monitoring efficiency, and facilitate better management of reservoir populations.

Geophysical and Geochemical Exploration of the Pockmark Field in the Gulf of Patras: New Insights on Formation, Growth and Activity

The Patras Gulf Pockmark field is located in shallow waters offshore Patras City (Greece) and is considered one of the most spectacular and best-documented fluid seepage activities in the Ionian Sea. The field has been under investigation since 1996, though surveying was partially sparse and fragmentary. This paper provides a complete mapping of the field and generates new knowledge regarding the fluid escape structures, the fluid pathways, their origin and the link with seismic activity. For this, data sets were acquired utilising high-resolution marine remote sensing techniques, including multibeam echosounders, side-scan sonars, sub-bottom profilers and remotely operated vehicles, and laboratory techniques focusing on the chemical composition of the escaping fluids. The examined morphometric parameters and spatial distribution patterns of the pockmarks are directly linked to tectonic structures. Acoustic anomalies related to the presence of gas in sediments and in the water column document the activity of the field at present and in the past. Methane is the main component of the fluids and is of microbial origin. Regional and local tectonism, together with the Holocene sedimentary deposits, appear to be the main contributors to the growth of the field. The field preserves evidence that earthquake activity prompts the activation of the field.

Data Generation with GAN Networks for Sidescan Sonar in Semantic Segmentation Applications

In the realm of underwater exploration, particularly within the domain of autonomous detection, sidescan sonar stands as a pivotal sensor apparatus. Autonomous detection models necessitate a substantial volume of scanned sonar image data for optimal training, yet the challenges and costs associated with acquiring such data pose significant limitations on the deployment of autonomous detection models in underwater exploration scenarios. Consequently, there arises a demand for the development of cost-effective data augmentation techniques. In the present investigation, an initial collection of scanned sonar image data was conducted during lake trials, encompassing diverse environmental regions, including rocky terrain, shadowed areas, and aquatic bodies. Subsequently, a proprietary generative adversarial network (GAN) model was devised for the purpose of synthesizing scanned sonar data. The synthesized data underwent denoising and underwent post-processing via algorithmic methods. Subsequently, similarity metrics were computed to gauge the quality of the generated scanned sonar data. Furthermore, a semantic segmentation model was meticulously crafted and trained by employing authentic data. The generated data were subsequently introduced into this semantic segmentation model. The output outcomes demonstrated that the model exhibited preliminary labeling proficiency on the generated image data, requiring only minimal manual intervention to conform to the standards of a conventional dataset. Following the inclusion of the labeled data into the original dataset and the subsequent training of the network model utilizing the expanded dataset, there was an observed discernible enhancement in the segmentation performance of the model.

An Underwater Side-Scan Sonar Transfer Recognition Method Based on Crossed Point-to-Point Second-Order Self-Attention Mechanism

Recognizing targets through side-scan sonar (SSS) data by deep learning-based techniques has been particularly challenging. The primary challenge stems from the difficulty and time consumption associated with underwater acoustic data acquisition, which demands systematic explorations to obtain sufficient training samples for accurate deep learning-based models. Moreover, if the sample size of the available data is small, the design of effective target recognition models becomes complex. These challenges have posed significant obstacles to developing accurate SSS-based target recognition methods via deep learning models. However, utilizing multi-modal datasets to enhance the recognition performance of sonar images through knowledge transfer in deep networks appears promising. Owing to the unique statistical properties of various modal images, transitioning between different modalities can significantly increase the complexity of network training. This issue remains unresolved, directly impacting the target transfer recognition performance. To enhance the precision of categorizing underwater sonar images when faced with a limited number of mode types and data samples, this study introduces a crossed point-to-point second-order self-attention (PPCSSA) method based on double-mode sample transfer recognition. In the PPCSSA method, first-order importance features are derived by extracting key horizontal and longitudinal point-to-point features. Based on these features, the self-supervised attention strategy effectively removes redundant features, securing the second-order significant features of SSS images. This strategy introduces a potent low-mode-type small-sample learning method for transfer learning. Classification experiment results indicate that the proposed method excels in extracting key features with minimal training complexity. Moreover, experimental outcomes underscore that the proposed technique enhances recognition stability and accuracy, achieving a remarkable overall accuracy rate of 99.28%. Finally, the proposed method maintains high recognition accuracy even in noisy environments.

Real-Time Processing and High-Quality Imaging of Navigation Strip Data Using SSS Based on AUVs

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

Assessing the effect of onshore and offshore Wave Energy Converters on seafloor integrity combining image-based and acoustic methods

The European Atlantic Ocean offers great opportunities for the development of projects for renewable energy extraction, and the Marine Renewable Energy sector is developing different technologies for energy converters, including Wave Energy Converters. Besides, the European Commission is adopting measures and politics to increase the installed capacity of ocean energy. However, there are still uncertainties on the potential environmental effects of wave energy converters, which led regulators and stakeholders to perceive their operation as a risky activity. To overcome the non-technological barriers that could hinder the development of marine renewable energies, and improve the knowledge on the impacts on the seafloor integrity (among others), SafeWAVE project set an Environmental Research Demonstration Strategy based on the collection, processing, modelling, analysis and sharing of environmental data collected in Aguçadoura (Portugal), Armintza (Spain) and Le Croisic (France) representing different types of technology, locations and project scales. Video recordings were carried out, using Remotely Operated Vehicles, to identify the environmental impacts of the moorings on the seafloor morphology. That information was completed by side scan sonar campaigns using an Autonomous Underwater Vehicle. The video recordings were useful also to assess the attraction effect of the moorings to epibenthic invertebrates and fishes, whereas the side scan sonar provided quantitative information on the area affected by physical alteration (<1% of the total area occupied by the devices at Armintza and Le Croisic).

The Ability to Detect Sound Scattering Layers by Interferometric Side-Scan Sonar

A method and algorithm for remote detection of sound-scattering layers in the seas and oceans using strip survey data of the bottom topography by interferometric side-scan sonar (ISSS) is considered. Based on mathematical modeling of phase-difference measurements of ISSS for multilayer scattering planes in seawater, we demonstrate the possibility of detecting sound-scattering layers and measuring their depths using the proposed algorithm. The accuracy of calculating the depths of sound-dispersing layers at a constant value of the sound velocity has been evaluated and the requirements to the ISSS have been determined to ensure the necessary accuracy of detection of individual layers located at different depths. The efficiency of the proposed method is demonstrated based on the example of processing by the developed algorithm of the experimental data obtained during bottom relief studies with an 85 kHz range ISSS.

A convolutional vision transformer for semantic segmentation of side-scan sonar data

Distinguishing among different marine benthic habitat characteristics is of key importance in a wide set of seabed operations ranging from installations of oil rigs to laying networks of cables and monitoring the impact of humans on marine ecosystems. The Side-Scan Sonar (SSS) is a widely used imaging sensor in this regard. It produces high-resolution seafloor maps by logging the intensities of sound waves reflected back from the seafloor. In this work, we leverage these acoustic intensity maps to produce pixel-wise categorization of different seafloor types. We propose a novel architecture adapted from the Vision Transformer (ViT) in an encoder–decoder framework. Further, in doing so, the applicability of ViTs is evaluated on smaller datasets. To overcome the lack of CNN-like inductive biases, thereby making ViTs more conducive to applications in low data regimes, we propose a novel feature extraction module to replace the Multi-layer Perceptron (MLP) block within transformer layers and a novel module to extract multiscale patch embeddings. A lightweight decoder is also proposed to complement this design in order to further enhance multiscale feature extraction. With the modified architecture, we achieve state-of-the-art results and also meet real-time computational requirements. We make our code available at https://github.com/hayatrajani/s3seg-vit.

Geophysical surveys and satellite imaging for the evaluation of near‐surface terrain dynamic – A case study on Grand Portage, MN, USA

AbstractFlooding, erosion and increases in the water level in Lake Superior have contributed to changes in the stem location and width of the Grand Portage Creek. Those events threaten parts of the Grand Portage National Monument, a historically significant site on the North Shore of Lake Superior, Minnesota. We performed geophysical surveys to investigate these dynamic effects. We studied the near‐surface geological deposits, the mechanisms associated with creek stem dynamics and sediment transport, as well as deposition along the lakeshore in Grand Portage Bay. We deployed ground‐penetrating radar (GPR), sub‐bottom profiler, side scan sonar, Geoprobe coring and Van Veen grab samplers and evaluated time‐lapse satellite images to assess the interaction of the Grand Portage Creek with the Grand Portage Bay. The onshore GPR surveys next to the national monument, the creek and the shoreline showed the presence of a complex deposition with eroded ground surfaces and sediment layers across the creek valley. Results from the offshore geophysical campaigns and the interpretations of satellite images also document a heterogeneous deposition sequence environment with fine‐grained sediment deposits present south and southwest of the creek mouth. In addition, we documented an exposed boulder bed towards the east of the creek mouth that was exposed by the current and wave‐driven erosion process in the Grand Portage Bay. Time‐lapse satellite images and hydraulic current velocity simulations validate these observations and provide insight into how anthropogenic activities and natural events interact and might contribute to the long‐term stability of a site of historical and cultural importance.

Improved Adaptive Finch Clustering Sonar Segmentation Algorithm Based on Data Distribution and Posterior Probability

This study proposes a side-scan sonar target detection technique for CPU or low-performance GPU to meet the requirement of underwater target detection. To rectify the gray distribution of the original side scan sonar data, enhance picture segmentation, and supply the data distribution probability for the clustering algorithm, the methodology uses a classic image processing technique that is GPU-friendly. The modified adaptive Finch clustering technique is used to segment the image and remove image voids after assessing the processed image attributes. The posterior information is then used to apply a classification label to each pixel. The characteristics of the connected region are analyzed in the data playback of the Tuandao experiment in accordance with the imaging principle of side-scan sonar and the original shape and size characteristics of the target. The predicted target results are combined with the AUV navigation information to obtain the predicted target longitude and latitude information, which is then sent to the AUV master control system to guide the next plan. The Jiaozhou Bay sea test results demonstrate that the traditional target detection algorithm put forth in this paper can be integrated into a low-performance GPU to detect targets and locate them. The detection accuracy and speed exhibit strong performance, and real-time autonomous sonar detection is made possible.

The cold-water coral province of the eastern Ligurian Sea (NW Mediterranean Sea): historical and novel evidences

Several bathyal cold-water coral provinces, characterized by a lush growth of habitat-forming scleractinians, have been recognized in the Mediterranean Sea. However, the search for this biogenic habitat only marginally targeted the Italian coast of the Ligurian Sea (NW Mediterranean basin) despite historical and a few recent local studies in the region reporting the presence of corals. This study used bathymetry maps, side-scan sonar profiles, historical charts, and trawling routes to identify sites that could potentially host coral habitats in the eastern sector of the Ligurian Sea. Remotely operated vehicle video footage from various projects (2015-2021, 20 dives) was then used to characterize four sub-areas (Genoa Plateau, Portofino, Deiva Marina, and Monterosso) where corals were detected between 450 m and 750 m depth. Radiocarbon dating was used to trace back the geological history of the coral structures. A small coral mound, impacted by trawling activities, was found on the Genoa Plateau, while four massive coral structures were found in the other sub-areas, mainly located in a morphologically complex and highly energetic canyon region. High levels of megafaunal biodiversity, including rarely reported alcyonaceans as Placogorgia coronata, were observed together with moderate fishing impact. Overall, the identified coral areas potentially account for 9 km2 of both subfossil mounds (as old as 13300 years BP), dominated by Desmophyllum pertusum, and living reefs, dominated by Madrepora oculata, the latter representing up to 23% of the substrate coverage. The few living colonies of D. pertusum in the area represent the first documented records for the Ligurian Sea. These data support the presence of a distinct eastern Ligurian cold-water coral province.

Evaluating the use of side scan sonar for improved detection and targeted retrieval of abandoned, lost, or otherwise discarded fishing gear

Abandoned, lost, or otherwise discarded fishing gear (ALDFG) has surfaced as a significant conservation issue that continues to compromise the economic, social, and ecological aspects of the marine environment. To alleviate these concerns, methods of lost gear detection at the seabed can be applied to increase the precision of derelict gear retrieval and potentially improve the likelihood of success. Targeted in Canada's most productive American lobster (Homarus americanus) fishing area, a “hotspot” analysis was performed by mapping the density of reported lost gear at a 3 km × 3 km grid resolution. The hotspot analysis was used to target the collection of 27 side scan sonar (SSS) transects in Lobster Fishing Area (LFA) 34 over a 12-day survey period in Clark's Harbour, Nova Scotia, to evaluate the benefits of gear detection in large-scale retrieval missions. Following a comprehensive review of the SSS data post-processing, 114 potential ALDFG targets were visually identified, and only one item was confirmed retrieved. Despite this, a large volume of ALDFG was retrieved in areas where there was no SSS coverage based on fisher's local knowledge. The findings from this study demonstrate that retrieval of ALDFG based on SSS data can be difficult due to limitations of retrieval methods used, and that costs of SSS data acquisition may prohibit widescale use of this technology on an annual basis in spatially extensive fishing areas. Nonetheless, the use of SSS to identify ALDFG is recommended at smaller geographic scales in areas where the benthic environment may be sensitive. Although the use of fisher's local knowledge was not evaluated in this study, where the impacts of grappling for ALDFG are considered minimal, local knowledge can be extremely effective in the removal of lost fishing gear and other debris from the benthic environment.

Guest editorial: recent advances in synthetic aperture sonar technology

Abstract[Correction added on 28‐July‐2023, after first online publication: The first affiliation has been changed and bio for Gary Heald has been added in this version.]Compared to traditional sidescan sonar, synthetic aperture sonar (SAS) or Interferometric synthetic aperture sonar (InSAS) systems can provide much higher resolution images across the whole swath. Due to the high resolution and fast mapping rate at a given speed, SAS/InSAS technology is gaining increasing interest within the field of underwater engineering. Navigation, autonomous underwater vehicles (AUVs), sonar technolog and electronics have achieved great progress in recent years. Thanks to these developments, the SAS/InSAS has also been pushed into a new stage. Firstly, the lightweight and compact SAS can be mounted on ever smaller, highly flexible platforms. Secondly, current algorithms are computationally more efficient. Thirdly, the SAS/InSAS images are further improved based on various methods. Last but not least, SAS/InSAS technology is preferred by underwater mapping users due to its high resolution. The current Special Issue is focused on research ideas, articles and experimental studies related to “Recent Advances in Synthetic Aperture Sonar Technology” for development, operating mode, algorithms and applications the various aspects of SAS/InSAS in applications.

Side-scan sonar underwater target segmentation using the BHP-UNet

Although target detection algorithms based on deep learning have achieved good results in the detection of side-scan sonar underwater targets, their false and missed detection rates are high for multiple densely arranged and overlapping underwater targets. To address this problem, a side-scan sonar underwater target segmentation model based on the blended hybrid dilated convolution and pyramid split attention UNet (BHP-UNet) algorithm is proposed in this paper. First, the blended hybrid dilated convolution module is adopted to improve the ability of the model to learn deep semantics and shallow features while improving the receptive field. Second, the pyramid split attention module is introduced to establish a long-term dependency between global and local information while processing multi-scale spatial features. Three sets of experimental results show that the BHP-UNet model proposed in this paper has better segmentation performance than the conventional fully convolutional network, UNet, and DeepLabv3+ models, and it is able to segment dense and overlapping targets to a certain extent. The proposed model will have significance as a guide for practical applications.