Benthic habitat mapping: A review of three decades of mapping biological patterns on the seafloor

Misiuk, Benjamin; Brown, Craig J.

doi:10.1016/j.ecss.2023.108599

Cited by 5 publications

(2 citation statements)

References 303 publications

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“…Moreover, Li et al (2020) [24] explored the effectiveness of deep learning frameworks in marine remote sensing image classification, demonstrating how these frameworks can extract information from marine remote sensing images through eight typical applications. Misiuk et al (2023) [25] reviewed the relevant developments of underwater surveying and mapping technology in the past 30 years and pointed out the future development directions of related technologies. In underwater object classification, Gav et al (2015) [26] focused on using ultrasonic technology for classification, highlighting the effectiveness of different methods such as Bayesian and Principal Component Analysis (PCA) in achieving underwater object classification.…”

Section: Introductionmentioning

confidence: 99%

Side-Scan Sonar Image Matching Method Based on Topology Representation

Yang,

Yu,

Wang

et al. 2024

JMSE

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In the realm of underwater environment detection, achieving information matching stands as a pivotal step, forming an indispensable component for collaborative detection and research in areas such as distributed mapping. Nevertheless, the progress in studying the matching of underwater side-scan sonar images has been hindered by challenges including low image quality, intricate features, and susceptibility to distortion in commonly used side-scan sonar images. This article presents a comprehensive overview of the advancements in underwater sonar image processing. Building upon the novel SchemaNet image topological structure extraction model, we introduce a feature matching model grounded in side-scan sonar images. The proposed approach employs a semantic segmentation network as a teacher model to distill the DeiT model during training, extracting the attention matrix of intermediate layer outputs. This emulates SchemaNet’s transformation method, enabling the acquisition of high-dimensional topological structure features from the image. Subsequently, utilizing a real side-scan sonar dataset and augmenting data, we formulate a matching dataset and train the model using a graph neural network. The resulting model demonstrates effective performance in side-scan sonar image matching tasks. These research findings bear significance for underwater detection and target recognition and can offer valuable insights and references for image processing in diverse domains.

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Section: Introductionmentioning

confidence: 99%

Side-Scan Sonar Image Matching Method Based on Topology Representation

Yang,

Yu,

Wang

et al. 2024

JMSE

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“…Mapping sediment as a continuous variable instead of a discrete one may allow for more accurate estimates of species distributions when it is used as a predictor (Wilson et al, 2018). Modern quantitative modelling approaches can offer an alternative way to produce continuous coverage maps depicting gradational changes in substrate parameters, and may additionally be used to infer sediment composition in areas where ground truth validation is not available (Misiuk and Brown, 2024). This can be achieved by using geospatial models that treat substrate parameters as a response variable to be predicted using continuous coverage environmental data sets (e.g.…”

Section: Introductionmentioning

confidence: 99%

Seafloor morphology and substrate mapping in the Gulf of St Lawrence, Canada, using machine learning approaches

Sklar,

Bushuev,

Misiuk

et al. 2024

Front. Mar. Sci.

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Detailed maps of seafloor substrata and morphology can act as valuable proxies for predicting and understanding the distributions of benthic communities and are important for guiding conservation initiatives. High resolution acoustic remote sensing data can facilitate the production of detailed seafloor maps, but are cost-prohibitive to collect and not widely available. In the absence of targeted high resolution data, global bathymetric data of a lower resolution, combined with legacy seafloor sampling data, can provide an alternative for generating maps of seafloor substrate and morphology. Here we apply regression random forest to legacy data in the Gulf of St Lawrence, Canada, to generate a map of seabed sediment distribution. We further apply k-means clustering to a principal component analysis output to identify seafloor morphology classes from the GEBCO bathymetric grid. The morphology classification identified most morphological features but could not discriminate valleys and canyons. The random forest results were in line with previous sediment mapping work done in the area, but a large proportion of zero values skewed the explained variance. In both models, improvements may be possible with the introduction of more predictor variables. These models prove useful for generating regional seafloor maps that may be used for future management and conservation applications.

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