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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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Nonlin. Processes Geophys., 24, 367-378, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
20 Jul 2017
Regularization destriping of remote sensing imagery
Ranil Basnayake1,2, Erik Bollt1,2, Nicholas Tufillaro3, Jie Sun1,2, and Michelle Gierach4 1Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
2Clarkson Center for Complex Systems Science (CS), Potsdam, NY 13699, USA
3College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
Abstract. We illustrate the utility of variational destriping for ocean color images from both multispectral and hyperspectral sensors. In particular, we examine data from a filter spectrometer, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (NPP) orbiter, and an airborne grating spectrometer, the Jet Population Laboratory's (JPL) hyperspectral Portable Remote Imaging Spectrometer (PRISM) sensor. We solve the destriping problem using a variational regularization method by giving weights spatially to preserve the other features of the image during the destriping process. The target functional penalizes the neighborhood of stripes (strictly, directionally uniform features) while promoting data fidelity, and the functional is minimized by solving the Euler–Lagrange equations with an explicit finite-difference scheme. We show the accuracy of our method from a benchmark data set which represents the sea surface temperature off the coast of Oregon, USA. Technical details, such as how to impose continuity across data gaps using inpainting, are also described.

Citation: Basnayake, R., Bollt, E., Tufillaro, N., Sun, J., and Gierach, M.: Regularization destriping of remote sensing imagery, Nonlin. Processes Geophys., 24, 367-378,, 2017.
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