Record Details

Ocean wavenumber estimation from wave-resolving time series imagery

ScholarsArchive at Oregon State University

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Title Ocean wavenumber estimation from wave-resolving time series imagery
Names Plant, Nathaniel G. (creator)
Holland, K. Todd (creator)
Haller, Merrick C. (creator)
Date Issued 2008-09 (iso8601)
Note Article appears in IEEE Transactions on Geoscience and Remote Sensing and is copyrighted by IEEE.
Abstract We review several approaches that have been used to
estimate ocean surface gravity wavenumbers from wave-resolving
remotely sensed image sequences. Two fundamentally different
approaches that utilize these data exist. A power spectral density
approach identifies wavenumbers where image intensity variance
is maximized. Alternatively, a cross-spectral correlation approach
identifies wavenumbers where intensity coherence is maximized.
We develop a solution to the latter approach based on a tomographic
analysis that utilizes a nonlinear inverse method. The
solution is tolerant to noise and other forms of sampling deficiency
and can be applied to arbitrary sampling patterns, as well as to
full-frame imagery. The solution includes error predictions that
can be used for data retrieval quality control and for evaluating
sample designs. A quantitative analysis of the intrinsic resolution
of the method indicates that the cross-spectral correlation fitting
improves resolution by a factor of about ten times as compared
to the power spectral density fitting approach. The resolution
analysis also provides a rule of thumb for nearshore bathymetry
retrievals—short-scale cross-shore patterns may be resolved if
they are about ten times longer than the average water depth
over the pattern. This guidance can be applied to sample design to
constrain both the sensor array (image resolution) and the analysis
array (tomographic resolution).
Genre Article
Topic adaptive signal processing
Identifier Plant, N. G., Holland, K. T., & Haller, M. C. (2008). Ocean wavenumber estimation from wave-resolving time series imagery [Electronic version]. IEEE Transactions on Geoscience and Remote Sensing, 46(9), 2644-2658.

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