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Automated Riverbed Sediment Classification Using Low-Cost Sidescan Sonar
The use of low-cost, low-profile, and highly portable sidescan sonar is on the ascendancy for imaging shallow riverine benthic sediments. A new automated, spatially explicit, and physically-based method for calculating lengthscales of bed texture elements in sidescan echograms (a 2D plot of acoustic intensity as a function of slant range and distance) is suggested. It uses spectral analysis based on the wavelet transform of short sequences of echograms. The recursive application of the transform over small overlapping windows of the echogram provides a robust measure of lengthscales of alternating patterns of strong and weak echoes. This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical representation that integrates over many attributes of bed texture, of which grain size is the most important. The technique is a physically-based means to identify regions of texture within a sidescan echogram, and could provide a basis for objective, automated riverbed sediment classification. Results are evaluated using data from two contrasting riverbed environments: those of the Colorado River in Grand Canyon, Arizona, and the West Branch of the Penobscot River, Maine.
Automated Riverbed Sediment Classification Using Low-Cost Sidescan Sonar
The use of low-cost, low-profile, and highly portable sidescan sonar is on the ascendancy for imaging shallow riverine benthic sediments. A new automated, spatially explicit, and physically-based method for calculating lengthscales of bed texture elements in sidescan echograms (a 2D plot of acoustic intensity as a function of slant range and distance) is suggested. It uses spectral analysis based on the wavelet transform of short sequences of echograms. The recursive application of the transform over small overlapping windows of the echogram provides a robust measure of lengthscales of alternating patterns of strong and weak echoes. This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical representation that integrates over many attributes of bed texture, of which grain size is the most important. The technique is a physically-based means to identify regions of texture within a sidescan echogram, and could provide a basis for objective, automated riverbed sediment classification. Results are evaluated using data from two contrasting riverbed environments: those of the Colorado River in Grand Canyon, Arizona, and the West Branch of the Penobscot River, Maine.
Automated Riverbed Sediment Classification Using Low-Cost Sidescan Sonar
Buscombe, Daniel (author) / Grams, Paul E. (author) / Smith, Sean M. C. (author)
2015-09-23
Article (Journal)
Electronic Resource
Unknown
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