Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method

Handle URI:
http://hdl.handle.net/10754/622838
Title:
Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method
Authors:
Yin, Gaohong ( 0000-0002-0234-0688 ) ; Mariethoz, Gregoire; McCabe, Matthew ( 0000-0002-1279-5272 )
Abstract:
The failure of the Scan Line Corrector (SLC) on Landsat 7 imposed systematic data gaps on retrieved imagery and removed the capacity to provide spatially continuous fields. While a number of methods have been developed to fill these gaps, most of the proposed techniques are only applicable over relatively homogeneous areas. When they are applied to heterogeneous landscapes, retrieving image features and elements can become challenging. Here we present a gap-filling approach that is based on the adoption of the Direct Sampling multiple-point geostatistical method. The method employs a conditional stochastic resampling of known areas in a training image to simulate unknown locations. The approach is assessed across a range of both homogeneous and heterogeneous regions. Simulation results show that for homogeneous areas, satisfactory results can be obtained by simply adopting non-gap locations in the target image as baseline training data. For heterogeneous landscapes, bivariate simulations using an auxiliary variable acquired at a different date provides more accurate results than univariate simulations, especially as land cover complexity increases. Apart from recovering spatially continuous fields, one of the key advantages of the Direct Sampling is the relatively straightforward implementation process that relies on relatively few parameters.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Water Desalination and Reuse Research Center (WDRC)
Citation:
Yin G, Mariethoz G, McCabe M (2016) Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method. Remote Sensing 9: 12. Available: http://dx.doi.org/10.3390/rs9010012.
Publisher:
MDPI AG
Journal:
Remote Sensing
Issue Date:
28-Dec-2016
DOI:
10.3390/rs9010012
Type:
Article
ISSN:
2072-4292
Sponsors:
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).
Additional Links:
http://www.mdpi.com/2072-4292/9/1/12
Appears in Collections:
Articles; Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorYin, Gaohongen
dc.contributor.authorMariethoz, Gregoireen
dc.contributor.authorMcCabe, Matthewen
dc.date.accessioned2017-02-07T08:28:37Z-
dc.date.available2017-02-07T08:28:37Z-
dc.date.issued2016-12-28en
dc.identifier.citationYin G, Mariethoz G, McCabe M (2016) Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method. Remote Sensing 9: 12. Available: http://dx.doi.org/10.3390/rs9010012.en
dc.identifier.issn2072-4292en
dc.identifier.doi10.3390/rs9010012en
dc.identifier.urihttp://hdl.handle.net/10754/622838-
dc.description.abstractThe failure of the Scan Line Corrector (SLC) on Landsat 7 imposed systematic data gaps on retrieved imagery and removed the capacity to provide spatially continuous fields. While a number of methods have been developed to fill these gaps, most of the proposed techniques are only applicable over relatively homogeneous areas. When they are applied to heterogeneous landscapes, retrieving image features and elements can become challenging. Here we present a gap-filling approach that is based on the adoption of the Direct Sampling multiple-point geostatistical method. The method employs a conditional stochastic resampling of known areas in a training image to simulate unknown locations. The approach is assessed across a range of both homogeneous and heterogeneous regions. Simulation results show that for homogeneous areas, satisfactory results can be obtained by simply adopting non-gap locations in the target image as baseline training data. For heterogeneous landscapes, bivariate simulations using an auxiliary variable acquired at a different date provides more accurate results than univariate simulations, especially as land cover complexity increases. Apart from recovering spatially continuous fields, one of the key advantages of the Direct Sampling is the relatively straightforward implementation process that relies on relatively few parameters.en
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).en
dc.publisherMDPI AGen
dc.relation.urlhttp://www.mdpi.com/2072-4292/9/1/12en
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectGap fillingen
dc.subjectLandsat ETM+en
dc.subjectMultiple-point geostatisticsen
dc.subjectScan line correctoren
dc.subjectSLCen
dc.titleGap-Filling of Landsat 7 Imagery Using the Direct Sampling Methoden
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.identifier.journalRemote Sensingen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionInstitute of Earth Surface Dynamics, University of Lausanne, Lausanne, 1015, Switzerlanden
kaust.authorYin, Gaohongen
kaust.authorMcCabe, Matthewen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.