Regularization destriping of remote sensing imagery

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 EulerLagrange 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 in painting, are also described.

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Document Details

Document Type
Technical Report
Publication Date
Jul 20, 2017
Accession Number
AD1082576

Entities

People

  • Erik Bollt
  • Jie Sun
  • Michelle Gierach
  • Nicholas Tufillaro
  • Ranil Basnayake

Organizations

  • Clarkson University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Computational Science
  • Computations
  • Data Processing
  • Data Sets
  • Detectors
  • Equations
  • Focal Planes
  • Graphs
  • Image Processing
  • Intensity
  • Inverse Problems
  • Mathematics
  • Remote Sensing
  • Sea Surface Temperature
  • Surface Temperature

Fields of Study

  • Physics

Readers

  • Computer Vision.
  • Image Processing and Computer Vision.
  • Operations Research

Technology Areas

  • Space