Side-scan sonar image processing: Seabed classification based on acoustic backscattering

Subarsyah,; Manik, Henry M.; Albab, Ali

doi:10.1088/1755-1315/944/1/012001

Cited by 5 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, to generate acoustic images, the acoustic backscatter intensity is converted into the pixel value of a grayscale image (called the backscatter mosaic). Finally, the backscatter strength is complex data, depending on several factors, such as the incident angle of the sound waves, the roughness of the detected object/material, and the medium [53,54]. While side-scan sonar provides high-resolution images of the seafloor, backscattering provides data about the composition of the seabed based on the intensity of echoes.…”

mentioning

confidence: 99%

A Survey of Seafloor Characterization and Mapping Techniques

Loureiro,

Dias,

Almeida

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed’s features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to address these tasks; however, each sensor has limitations associated with the underwater environment. This paper presents a survey of the main techniques and trends related to seabed characterization, highlighting approaches in three tasks: classification, detection, and segmentation. The bibliography is categorized into four approaches: statistics-based, classical machine learning, deep learning, and object-based image analysis. The differences between the techniques are presented, and the main challenges for deep sea research and potential directions of study are outlined.

show abstract

mentioning

confidence: 99%

A Survey of Seafloor Characterization and Mapping Techniques

Loureiro,

Dias,

Almeida

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

3D UHR seismic and back-scattering analysis for seabed and ultra-shallow subsurface classification

Ha,

Shin,

Lim

et al. 2024

Acta Geophys.

View full text Add to dashboard Cite

Recently, the seabed classification method based on back-scattering data of multi-beam echo-sounder (MBES) is widely used to analyze the distribution of seabed sediment. Although various analysis methods for seabed classification using multi-spectral MBES have been developed, they are limited in securing penetration depth to consider the characteristics of the shallow subsurface structure. In this study, the seabed and ultra-shallow subsurface classification was performed by comparative analysis of box corer sampling, back-scattering, and 2D/3D ultra-high-resolution (UHR) seismic data obtained from Yeongil Bay, South Korea. We proposed a process for seismic ultra-shallow subsurface classification by the segmentation of the primary seabed reflection wavelet and the amplitude analysis. The seabed-reflected amplitude and back-scattering intensity showed similar mapping trends in the relatively homogeneous and thick surface sediment. On the other hand, it was confirmed that back-scattering data and seabed-reflected amplitude show different patterns when the subsurface structure is related to the seabed surface. It is presumed that because seismic data containing relatively low-frequency components have a deeper penetration depth than MBES, they contain more characteristics of the ultra-shallow subsurface than back-scattering data. These were determined that back-scattering has advantages in representing acoustic anomaly distribution by surface sediment type, and seabed-reflected amplitude is advantageous for representing sediment type by ultra-shallow subsurface. In particular, these results were well shown when the surface sediment thinly covered the rocky bottom. Therefore, it is necessary not only to analyze the back-scattering of MBES but also the ultra-shallow subsurface features through seismic data for valid seabed classification.

show abstract

Identification of Types and Thickness Layers of Seabed Sediments

Maulana Akbar,

Aldila Syariz

2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

The sea toll program is one of the developments in the water sector that has a major influence on the areas it passes through. The sea highway acts as sea transportation that is carried out regularly and connects various ports in the territory of Indonesia. With the sea highway, there will be increased activity in the port area which will certainly increase the risk of shipping safety. This encourages periodic mapping and identification to reduce shipping risks that can occur. One of the activities to conduct mapping and identification is by hydrographic survey. There are many acoustic instruments that can be used for mapping the bottom of the water, in this study a sub-bottom profiler and side scan sonar were used. From the results of the study, the depth in the Berlian Terminal dock pool area ranges from 3.1 to 13 meters. The average thickness of the sediment layer from each survey lane is 1.01 m, 0.93 m, 1.41 m, 1.20 m, 1.37 m, 1.86 m, 1.15 m. The results of side scan sonar processing of the seafloor image interpretation produced several features consisting of pitted seafloor, ship grounding, and dredged areas. The distribution of sediment types was successfully classified into three classes consisting of silty clay, silty loam, and silty sand. This result is supported by sediment sampling which shows that the majority of sediment types in the study area was silty.

show abstract

Side-scan sonar image processing: Seabed classification based on acoustic backscattering

Cited by 5 publications

References 9 publications

A Survey of Seafloor Characterization and Mapping Techniques

A Survey of Seafloor Characterization and Mapping Techniques

3D UHR seismic and back-scattering analysis for seabed and ultra-shallow subsurface classification

Identification of Types and Thickness Layers of Seabed Sediments

Contact Info

Product

Resources

About