Acoustic Seabed Classification

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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 (especially in shallow water).

Surficial seabed classification is concerned primarily with distinguishing marine benthic habitat characteristics (e.g. hard, soft, rough, smooth, mud, sand, clay, cobble) of the surveyed area. Multibeam echosounders, sidescan sonar systems and acoustic ground discrimination systems (AGDS) are the most commonly used technologies. Multibeam systems acquire both bathymetry (depth) and backscatter (intensity) data, with full bottom coverage possible. 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 processing and analysis methods have increased the range of applications for which multibeam systems can be used. 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 (Lockhart et. al., 2007). This has allowed multibeam backscatter data to be of a quality comparable to that of sidescan sonar imagery.

Different approaches to seabed classification can give different results depending on the algorithms used. These include image-based seabed classification methods such as texture analysis, artificial neural networks (ANN); and others such as angular response characterization (Hughes-Clarke et al., 1997). One well established approach is to adapt image processing methods traditionally used in satellite remote sensing to quantitatively analyze the multibeam backscatter intensity data. After image segmentation and classification, acoustic imagery can be used to discriminate between areas with different morphological properties. A fundamental point to note when using any classification method is that, no classification map is 100% accurate and some attempt must always be made to assess the accuracy (e.g. confusion matrix).

The classification map needs to be subject to ground-verification in order to identify the class compositions and bottom type. The functionality of Geographic Information Systems (GIS) can be used to integrate data from different sources, for example, integrating with ground truth data. This includes in-situ sediment grab sampling, the use of a dredge, trawl net, visual imagery or surveys using Remotely Operated Vehicles (ROVs). The seabed classification map can be combined with other acoustically (or other method) derived information about the area, such as fish distribution and abundance or vegetation characteristics, to establish habitat groups based on the associations. This allows classification maps derived from multibeam data to help characterize the seabed and more effectively manage its use.

The use of optical sensors has been restricted to depths less than 40 m due to absorption of electromagnetic radiation by water. Most recently, processing tools have been developed to classify data acquired using airborne bathymetric LiDAR systems, (QTC, 2008). Nevertheless, acoustics remain the preferred method of imaging the seafloor as data can be acquired over a much larger area (than in-situ sampling) from almost any depth.

Sub-surface seabed classification is commonly referred to as sub-bottom profiling and is generally used for geological assessment of the sub-surface characteristics, down to hundreds of meters, as necessary in the exploration for oil deposits.

External links[edit]

  1. What is habitat mapping? (www.searchmesh.net)
  2. Lawrence & Bates, 2001, Acoustic ground discrimination systems (AGDS)
  3. Lockhart et. al., 2007
  4. Hughes-Clarke et.al., 1997, Ocean Mapping Group, UNB
  5. QTC, 2008
  6. Applied Acoustics- Special Issue:The Application of Underwater Acoustics for Seabed Habitat Mapping
  7. Acoustic seabed classification: current practice and future directions
  8. Sediment Classification Software (2009)
  9. Acoustic Seabed Classification Systems (2001)
  10. Bottom Classification
  11. Acoustic Seabed Classification – Applications in Fisheries Science and Ecosystem Studies
  12. Seabed classification

Resources - seabed surface:

  1. Acoustic Seabed Classification Bibliography
  2. Quester Tangent (QTC) Marine
  3. Mapping European Seabed Habitats (MESH) Project
  4. RoxAnn Seabed Classification System
  5. ICES Study Group on Acoustic Seabed Classification
  6. Case Studies and References for Lakes, Rivers & Marine
  7. BioSonics VBT Seabed Classification Software
  8. Quester Tangent QTC IMPACT
  9. ECHOplus seabed discrimination
  10. GEOHAB - Marine Geological and Biological Habitat Mapping - Conferences