A recursive approach to multiscalar data interpolation of sparsely sampled sea surface measurements at different spatial resolutions
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Data publicació2008
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Abstract
In many oceanographic studies there is a need to
reconstruct a signal from a set of sparse measurements. We
propose an algorithm to iteratively approximate the
intermediate values between irregularly sampled data, when a
set of sparse values at coarser scales is known. This is possible
when there is an approximation to a model for the
multiresolution decomposition/reconstruction scheme of the
dataset. Although the problem is ill-posed, this approach gives
an easy scheme to interpolate the values of a signal using all
the information available at different resolutions. This
reconstruction method could be used as an extension of any
interpolation method to optimize the multiresolution sparse
data fusion. A simplified one-dimensional case illustrates the
explanation; it is an algorithm based on a recursive scheme of
a fast dyadic wavelet transform and its inversion, using a filter
bank analysis/synthesis implementation for the wavelet
transforms model. This can be a basis method suitable for
applied cases where there are sparse measures from different
instruments that are sensing the same scene simultaneously
with several resolutions. Extensions of the method to sparse
multiresolution dataset with higher dimensions (images or
vector fields) also offer some promising preliminary results.
CitacióReig, R. [et al.]. A recursive approach to multiscalar data interpolation of sparsely sampled sea surface measurements at different spatial resolutions. A: OCEANS MTS/IEEE. "OCEANS'08 MTS/IEEE KOBE-TECHNO-OCEAN'08 Conference and Exhibition : Voyage toward the future". Kobe: 2008.
ISBN978-1-4244-2126-8
Versió de l'editorhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4530949
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