A New Method for Acquisition of High-Resolution Seabed Topography by Matching Seabed Classification Images

Zhao, Jianhu; Meng, Junxia; Zhang, Hongmei; Yan, Jianguo

doi:10.3390/rs9121214

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Objects of the same along track distance but further apart from the sonar appear as one single object [80]. While MBES across-track resolution used to be lower than SSS resolution, the introduction of beam time series ("snippets") considerably increased MBES across track resolution [85]. Advantages of MBES backscatter investigation is the additional depth information which can be used to detect stones directly in bathymetric data, while a corresponding disadvantage of MBES remains the depth-dependent size of the acoustic footprint which may not be constant across an investigation area.…”

Section: Hydroacoustic Mapping Techniquesmentioning

confidence: 99%

Hydroacoustic Mapping of Geogenic Hard Substrates: Challenges and Review of German Approaches

et al. 2020

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Subtidal hard substrate habitats are unique habitats in the marine environment. They provide crucial ecosystem services that are socially relevant, such as water clearance or as nursery space for fishes. With increasing marine usage and changing environmental conditions, pressure on reefs is increasing. All relevant directives and conventions around Europe include sublittoral hard substrate habitats in any manner. However, detailed specifications and specific advices about acquisition or delineation of these habitats are internationally rare although the demand for single object detection for e.g., ensuring safe navigation or to understand ecosystem functioning is increasing. To figure out the needs for area wide hard substrate mapping supported by automatic detection routines this paper reviews existing delineation rules and definitions relevant for hard substrate mapping. We focus on progress reached in German approval process resulting in first hydroacoustic mapping advices. In detail, we summarize present knowledge of hard substrate occurrence in the German North Sea and Baltic Sea, describes the development of hard substrate investigations and state of the art mapping techniques as well as automated analysis routines.

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Section: Hydroacoustic Mapping Techniquesmentioning

confidence: 99%

Hydroacoustic Mapping of Geogenic Hard Substrates: Challenges and Review of German Approaches

et al. 2020

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“…SURF, as a scale invariant feature, can be used as both detectors and descriptors of interest points at any scale [45]. In multibeam water column image, SURF is also suitable for the detection and description of bubble plume target feature points [46].…”

Section: Surf Descriptors Extractionmentioning

confidence: 99%

Bubble Plume Target Detection Method of Multibeam Water Column Images Based on Bags of Visual Word Features

2022

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Bubble plumes, as main manifestations of seabed gas leakage, play an important role in the exploration of natural gas hydrate and other resources. Multibeam water column images have been widely used in detecting bubble plume targets in recent years because they can wholly record water column and seabed backscatter strengths. However, strong noises in multibeam water column images cause many issues in target detection, and traditional target detection methods are mainly used in optical images and are less efficient for noise-affected sonar images. To improve the detection accuracy of bubble plume targets in water column images, this study proposes a target detection method based on the bag of visual words (BOVW) features and support vector machine (SVM) classifier. First, the characteristics of bubble plume targets in water column images are analyzed, with the conclusion that the BOVW features can well express the gray scale, texture, and shape characteristics of bubble plumes. Second, the BOVW features are constructed following steps of point description extraction, description clustering, and feature encoding. Third, the quadratic SVM classifier is used for the recognition of target images. Finally, a procedure of bubble plume target detection in water column images is described. In the experiment using the measured data in the Strait of Georgia, the proposed method achieved 98.6% recognition accuracy of bubble plume targets in validation sets, and 91.7% correct detection rate of the targets in water column images. By comparison with other methods, the experimental results prove the validity and accuracy of the proposed method, and show potential applications of our method in the exploration and research on ocean resources.

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“…As shown in Figure 2., the samples are in various strength ranges and need to be normalized into the same range for the network training. These samples can be normalized by using the z-score to ensure that they are in the same range [28]. Given that the side scan data are usually recorded in a fixed range (e.g., 0 to 2 16 -1), the samples can be simply normalized by the equation below.…”

Section: Normalization Of Sonar Data Sequencesmentioning

confidence: 99%

Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks

2019

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As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method.height of the sonar from the seabed cannot be accurately obtained [7]. Inaccurate sonar heights will lead to inaccurate geocoding sonar images [8], confuse the water column information with the seabed information, and cause serious problems in applications of target recognition and segmentation [9][10][11], image interpretation [12,13], and seabed sediment classification [14][15][16]. The bottom tracking of side scan data can accurately obtain the sonar height from the seabed by finding the first echo that reaches the seabed. Meanwhile, real-time bottom tracking can quickly detect changes in sonar height and seabed terrain, and enhance the safety of sonar equipment and ship navigation.Side scan sonars can be installed on the vessel or towed close to the seabed from the survey ship. These sonars acquire high-resolution images by emitting sound pulses and recording the backscatter strengths from the water column and seabed [17]. The depth of the side scan sonar can be determined by the depth sensor, whereas the sonar height cannot be easily determined [18]. The sound wave transmits through the water column, and then arr...

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A New Method for Acquisition of High-Resolution Seabed Topography by Matching Seabed Classification Images

Cited by 6 publications

References 28 publications

Hydroacoustic Mapping of Geogenic Hard Substrates: Challenges and Review of German Approaches

Hydroacoustic Mapping of Geogenic Hard Substrates: Challenges and Review of German Approaches

Bubble Plume Target Detection Method of Multibeam Water Column Images Based on Bags of Visual Word Features

Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks

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