Processing, mosaicking and management of the Monterey Bay digital sidescan-sonar images

Chavez, Pat S.; Isbrecht, JoAnn; Galanis, Peter; Gabel, Gregory L; Sides, S.; Soltesz, D.; Ross, Stephanie L.; Velasco, Miguel G.

doi:10.1016/s0025-3227(01)00273-0

Cited by 37 publications

(21 citation statements)

References 8 publications

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“…Figure 2 simplifies the SSS transducer as a point source, and treats the propagation path as a straight line. After time varying gain (TVG) correction and radiation correction, the acoustic waves hitting on the seabed surface have the same incident energy [29][30][31]. For the homogeneous seabed, u is often set as a constant.…”

Section: Solution Of Reconstruction Modelmentioning

confidence: 99%

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Reconstructing Seabed Topography from Side-Scan Sonar Images with Self-Constraint

2018

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Abstract:To obtain the high-resolution seabed topography and overcome the limitations of existing topography reconstruction methods in requiring external bathymetric data and ignoring the effects of sediment variations and Side-Scan Sonar (SSS) image quality, this study proposes a method of reconstructing seabed topography from SSS images with a self-constraint condition. A reconstruction model is deduced by Lambert's law and the seabed scattering model. A bottom tracking method is put forward to get the along-track SSS towfish heights and the initial seabed topography in the SSS measuring area is established by combining the along-track towfish heights, towfish depths and tidal levels obtained from Global Navigation Satellite System (GNSS). The complete process of reconstructing seabed topography is given by taking the initial topography as self-constraint and the high-resolution seabed topography is finally obtained. Experiments verified the proposed method by the data measured in Zhujiang River, China. The standard deviation of less than 15 cm is achieved and the resolution of the reconstructed topography is about 60 times higher than that of the Digital Elevation Model (DEM) established by bathymetric data. The effects of noise, suspended bodies, refraction of wave in water column, sediment variation, the determination of iteration termination condition as well as the performance of the proposed method under these effects are discussed. Finally, the conclusions are drawn out according to the experiments and discussions. The proposed method provides a simple and efficient way to obtain high-resolution seabed topography from SSS images and is a supplement but not substitution for the existing bathymetric methods.

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Section: Solution Of Reconstruction Modelmentioning

confidence: 99%

“…(2) Preprocessing the SSS image by sound ray tracing, slant distance correction, radiometric distortion correction, gray equalization, etc. [29][30][31]. (3) Compute the topography gradients by Equation (6), the angle φ by Equation (7), and the seabed topography by Equation (13) using the self-constraint.…”

Section: Process Of Reconstructing 3d Seabed Topographymentioning

confidence: 99%

Reconstructing Seabed Topography from Side-Scan Sonar Images with Self-Constraint

2018

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“…After the completion of the transmitting pulse, the transducers are switched over to the receiving circuitry, and the continuous recording of the incoming acoustic signal begins. The signals scan a swath of the seafloor from a point just below the towfish to a limited distance away from the line of travel (slant range) on both sides [18]. As shown in Figure 1A, Point 1 is the location of the SSS transducer, whereas Point 2 is the seabed just below the towfish.…”

Section: The Side Scan Sonar Imaging Mechanismmentioning

confidence: 99%

A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps

et al. 2018

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Abstract:To accurately achieve side scan sonar (SSS) image target detection, a novel target detection algorithm based on a neutrosophic set (NS) and diffusion maps (DMs) is proposed in this paper. Firstly, the neutrosophic subset images were obtained by transforming the input SSS image into the NS domain. Secondly, the shadowed areas of the SSS image were detected using the single gray value threshold method before the diffusion map was calculated. Lastly, based on the diffusion map, the target areas were detected using the improved target scoring equation defined by the diffusion distance and texture feature. The experiments using SSS images of single clear and unclear targets, with or without shadowed areas, showed that the algorithm accurately detects targets. Experiments using SSS images of multiple targets, with or without shadowed areas, showed that no false or missing detections occurred. The target areas were also accurately detected in SSS images with complex features such as sand wave terrain. The accuracy and effectiveness of the proposed algorithm were assessed.

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“…Geometric correction aims to establish the actual position in the sonar side scan pixel image, where as radiometrically is related to the intensity of back and digital numbers assigned to each pixel (Chavez et al, 2002;Milkert and Fiedler, 2002).…”

Section: Sonar Scan Side Data Correctionmentioning

confidence: 99%

Seabed Detection Using Application Of Image Side Scan Sonar Instrument (Acoustic Signal)

Lubis

Kausarian

Anurogo

2017

J. Geoscience Eng. Environ. Technol.

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The importance of knowing the method for seabed detection using side-scan sonar images with sonar instrument is a much-needed requirement right now. This kind of threat also requires frequent sonar surveys in such areas. These survey operations need specific procedures and special equipment to ensure survey correctness. In this paper describes the method of observation and retrieval of marine imagery data using an acoustic signal method, to determine a target based on the sea. Side scan sonar is an instrument consisting of single beam transducer on both sides. Side scan sonar (SSS) is a sonar development that is able to show in two-dimensional images of the seabed surface with seawater conditions and target targets simultaneously. The side scan sonar data processing is performed through geometric correction to establish the actual position of the image pixel, which consists of bottom tracking, slant-range correction, layback correction and radiometric correction performed for the backscatter intensity of the digital number assigned to each pixel including the Beam Angle Correction (BAC), Automatic Gain Control (AGC), Time Varied Gain (TVG), and Empirical Gain Normalization (EGN).

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Processing, mosaicking and management of the Monterey Bay digital sidescan-sonar images

Cited by 37 publications

References 8 publications

Reconstructing Seabed Topography from Side-Scan Sonar Images with Self-Constraint

Reconstructing Seabed Topography from Side-Scan Sonar Images with Self-Constraint

A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps

Seabed Detection Using Application Of Image Side Scan Sonar Instrument (Acoustic Signal)

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