Using the Direct Sampling Multiple-Point Geostatistical Method for Filling Gaps in Landsat 7 ETM+ SLC-off Imagery

Handle URI:
http://hdl.handle.net/10754/609181
Title:
Using the Direct Sampling Multiple-Point Geostatistical Method for Filling Gaps in Landsat 7 ETM+ SLC-off Imagery
Authors:
Yin, Gaohong ( 0000-0002-0234-0688 )
Abstract:
Since the failure of the Scan Line Corrector (SLC) instrument on Landsat 7, observable gaps occur in the acquired Landsat 7 imagery, impacting the spatial continuity of observed imagery. Due to the highly geometric and radiometric accuracy provided by Landsat 7, a number of approaches have been proposed to fill the gaps. However, all proposed approaches have evident constraints for universal application. The main issues in gap-filling are an inability to describe the continuity features such as meandering streams or roads, or maintaining the shape of small objects when filling gaps in heterogeneous areas. The aim of the study is to validate the feasibility of using the Direct Sampling multiple-point geostatistical method, which has been shown to reconstruct complicated geological structures satisfactorily, to fill Landsat 7 gaps. The Direct Sampling method uses a conditional stochastic resampling of known locations within a target image to fill gaps and can generate multiple reconstructions for one simulation case. The Direct Sampling method was examined across a range of land cover types including deserts, sparse rural areas, dense farmlands, urban areas, braided rivers and coastal areas to demonstrate its capacity to recover gaps accurately for various land cover types. The prediction accuracy of the Direct Sampling method was also compared with other gap-filling approaches, which have been previously demonstrated to offer satisfactory results, under both homogeneous area and heterogeneous area situations. Studies have shown that the Direct Sampling method provides sufficiently accurate prediction results for a variety of land cover types from homogeneous areas to heterogeneous land cover types. Likewise, it exhibits superior performances when used to fill gaps in heterogeneous land cover types without input image or with an input image that is temporally far from the target image in comparison with other gap-filling approaches.
Advisors:
McCabe, Matthew ( 0000-0002-1279-5272 )
Committee Member:
Hong, Pei-Ying ( 0000-0002-4474-6600 ) ; Sun, Ying ( 0000-0001-6703-4270 )
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Environmental Science and Engineering Program
Program:
Environmental Science and Engineering
Issue Date:
May-2016
Type:
Thesis
Appears in Collections:
Environmental Science and Engineering Program; Theses; Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorMcCabe, Matthewen
dc.contributor.authorYin, Gaohongen
dc.date.accessioned2016-05-12T13:10:01Zen
dc.date.available2016-05-12T13:10:01Zen
dc.date.issued2016-05en
dc.identifier.urihttp://hdl.handle.net/10754/609181en
dc.description.abstractSince the failure of the Scan Line Corrector (SLC) instrument on Landsat 7, observable gaps occur in the acquired Landsat 7 imagery, impacting the spatial continuity of observed imagery. Due to the highly geometric and radiometric accuracy provided by Landsat 7, a number of approaches have been proposed to fill the gaps. However, all proposed approaches have evident constraints for universal application. The main issues in gap-filling are an inability to describe the continuity features such as meandering streams or roads, or maintaining the shape of small objects when filling gaps in heterogeneous areas. The aim of the study is to validate the feasibility of using the Direct Sampling multiple-point geostatistical method, which has been shown to reconstruct complicated geological structures satisfactorily, to fill Landsat 7 gaps. The Direct Sampling method uses a conditional stochastic resampling of known locations within a target image to fill gaps and can generate multiple reconstructions for one simulation case. The Direct Sampling method was examined across a range of land cover types including deserts, sparse rural areas, dense farmlands, urban areas, braided rivers and coastal areas to demonstrate its capacity to recover gaps accurately for various land cover types. The prediction accuracy of the Direct Sampling method was also compared with other gap-filling approaches, which have been previously demonstrated to offer satisfactory results, under both homogeneous area and heterogeneous area situations. Studies have shown that the Direct Sampling method provides sufficiently accurate prediction results for a variety of land cover types from homogeneous areas to heterogeneous land cover types. Likewise, it exhibits superior performances when used to fill gaps in heterogeneous land cover types without input image or with an input image that is temporally far from the target image in comparison with other gap-filling approaches.en
dc.language.isoenen
dc.subjectLandsaten
dc.subjectSLC-offen
dc.subjectgap-fillingen
dc.subjectMPSen
dc.titleUsing the Direct Sampling Multiple-Point Geostatistical Method for Filling Gaps in Landsat 7 ETM+ SLC-off Imageryen
dc.typeThesisen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberHong, Pei-Yingen
dc.contributor.committeememberSun, Yingen
thesis.degree.disciplineEnvironmental Science and Engineeringen
thesis.degree.nameMaster of Scienceen
dc.person.id132609en
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