Gap fill

Remote Sensing of Environment 124 (2012) 49–60

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Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse

A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images
Xiaolin Zhu a, b, Desheng Liu b, c,⁎, Jin Chen a a b c State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Department of Geography, The Ohio State University, Columbus, OH 43210, USA
Department of Statistics, The Ohio State University, Columbus, OH 43210, USA

a r t i c l e

i n f o

Article history:
Received 18 December 2011
Received in revised form 17 April 2012
Accepted 22 April 2012
Available online 25 May 2012
Keywords:
Landsat ETM+
SLC-off
Gap filling
Geostatistical
Kriging

a b s t r a c t
Since the failure of scan-line corrector (SLC) of the Landsat 7 Enhanced Thermal Mapper Plus (ETM+) sensor, a number of methods have been developed to fill the un-scanned gaps in ETM+ images. Unfortunately, the quality of the images filled by most of these existing methods is still not satisfactory, particularly in heterogeneous regions. Recently, a Neighborhood Similar Pixel Interpolator (NSPI) was developed that can accurately fill gaps in SLC-off images even in heterogeneous regions. However, the NSPI method is a type of deterministic interpolation approach that sets its weight parameters empirically and cannot provide statistical uncertainty of prediction. This study proposes a new gap-filling method called Geostatistical
Neighborhood Similar Pixel Interpolator (GNSPI) by improving the NSPI method using geostatistical theory.
The simulation study shows that: compared with previous geostatistical methods, the image filled by GNSPI has fewer striping effects; compared with NSPI, GNSPI is less empirical in its weight parameters and can provide uncertainty of prediction. More importantly, it can generate more accurate

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