An Effective Gap Filtering Method for Landsat ETM+ SLC-Off Data

  • Author(s): Seulki Lee, Minji Cho, and Changwook Lee
  • DOI: 10.3319/TAO.2016.07.18.02
  • Keywords: Landsat 7 ETM+, SLC-off, Gap-filled, Interpolation
  • Citation: Lee, S., M. Cho, and C. Lee, 2016: An effective gap filtering method for Landsat ETM+ SLC-off data. Terr. Atmos. Ocean. Sci., 27, 921-932, doi: 10.3319/TAO.2016.07.18.02
  • GIF method is proposed for Landsat 7 ETM+ SLC-off data correction
  • GIF method is improving the accuracy displaying land, sea and clouds area
  • GIF method reach to invaluable support to monitor system related with SLC-off data
Abstract

The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) scan line corrector (SLC) failed on 31 May 2003, causing the SLC to turn off. Many gap-filled products were developed and deployed to combat this situation. The majority of these products used a primary image taken by the SLC when functioning properly in an attempt to correct SLC-off images. However, temporal atmospheric elements could not be reliably reflected using a primary image, and therefore the corrected image was not viable for use by monitoring systems. To bypass this limitation, this study has developed the Gap Interpolation and Filtering (GIF) method that relies on one-dimensional interpolation filtering to conveniently recover pixels within a single image at a high level of accuracy without borrowing from images acquired at a different time or by another sensor. The GIF method was compared to two other methods—Global Linear Histogram Match (GLHM), and the Local Linear Histogram Match (LLHM)—both developed by National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) to determine its accuracy. The GIF method accuracy was found superior in land, sea, and cloud imaging. In particular, its sea and cloud images returned Root Mean Square Error (RMSE) values close to or less than 1. We expect the GIF method developed in this research to be of invaluable aid to monitoring systems that depend heavily on Landsat imagery.

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