Acoustic seabed classification
Acoustic seabed classification is the partitioning of a seabed acoustic image into discrete physical entities or classes. This is a particularly active area of development in the field of seabed mapping, marine geophysics, underwater acoustics and benthic habitat mapping. Seabed classification is one route to characterizing the seabed and its habitats. Seabed characterization makes the link between the classified regions and the seabed physical, geological, chemical or biological properties. Acoustic seabed classification is possible using a wide range of acoustic imaging systems including multibeam echosounders, sidescan sonar, single-beam echosounders, interferometric systems and sub-bottom profilers. Seabed classification based on acoustic properties can be divided into two main categories; surficial seabed classification and sub-surface seabed classification. Sub-surface imaging technologies use lower frequency sound to provide higher penetration, whereas surficial imaging technologies provide higher resolution imagery by utilizing higher frequencies.
Surficial seabed classification
Classification methods
Surficial seabed classification is concerned primarily with distinguishing marine benthic habitat characteristics of the surveyed area. Multibeam echosounders, sidescan sonar systems and are the most commonly used technologies. The use of optical sensors has been restricted to depths less than 40 m due to absorption of electromagnetic radiation by water. Despite this limitation, processing tools have been developed to classify data acquired using airborne bathymetric LiDAR systems. Nevertheless, acoustics remain the preferred method of imaging the seafloor because data can be acquired over a much larger area from almost any depth.Multibeam systems acquire both bathymetry and backscatter data. Multibeam backscatter was previously considered to be a by-product of a multibeam survey, with bathymetry being the primary information. Recent advances in multibeam backscatter acquisition, processing and analysis methods have increased the range of applications for which multibeam systems can be used and now allow the collection of spatially and temporally coincident multispectral multibeam backscatter. New methods of analyzing backscatter data, have increased its potential for seabed characterization. Backscatter data resolution has also increased significantly with the introduction of snippet data. Snippet data is raw backscatter time-series data for each beam footprint and each ping . These advances have allowed some multibeam backscatter data to achieve a quality comparable to that of sidescan sonar imagery.
Different classification approaches and algorithms can yield different results. These approaches include image-based seabed classification methods such as texture analysis, artificial neural networks ; and other methods, such as angular response characterization . Image processing methods traditionally used in satellite remote sensing are often adapted to quantitatively analyze multibeam backscatter intensity data. After image segmentation and classification, acoustic imagery can be used to discriminate between areas with different morphological properties. No classification method produces a map that is 100% accurate and some attempt must always be made to assess the accuracy of classification results.