A comparison of gap-filling approaches for Landsat-7 satellite data

Handle URI:
http://hdl.handle.net/10754/625993
Title:
A comparison of gap-filling approaches for Landsat-7 satellite data
Authors:
Yin, Gaohong ( 0000-0002-0234-0688 ) ; Mariethoz, Gregoire; Sun, Ying ( 0000-0001-6703-4270 ) ; McCabe, Matthew ( 0000-0002-1279-5272 )
Abstract:
The purpose of this study is to assess the relative performance of four different gap-filling approaches across a range of land-surface conditions, including both homogeneous and heterogeneous areas as well as in scenes with abrupt changes in landscape elements. The techniques considered in this study include: (1) Kriging and co-Kriging; (2) geostatistical neighbourhood similar pixel interpolator (GNSPI); (3) a weighted linear regression (WLR) algorithm; and (4) the direct sampling (DS) method. To examine the impact of image availability and the influence of temporal distance on the selection of input training data (i.e. time separating the training data from the gap-filled target image), input images acquired within the same season (temporally close) as well as in different seasons (temporally far) to the target image were examined, as was the case of using information only within the target image itself. Root mean square error (RMSE), mean spectral angle (MSA), and coefficient of determination (R-2) were used as the evaluation metrics to assess the prediction results. In addition, the overall accuracy (OA) and kappa coefficient (kappa) were used to assess a land-cover classification based on the gap-filled images. Results show that all of the gap-filling approaches provide satisfactory results for the homogeneous case, with R-2 > 0.93 for bands 1 and 2 in all cases and R-2 > 0.80 for bands 3 and 4 in most cases. For the heterogeneous example, GNSPI performs the best, with R-2 > 0.85 for all tested cases. WLR and GNSPI exhibit equivalent accuracy when a temporally close input image is used (i.e. WLR and GNSPI both have an R-2 equal to 0.89 for band 1). For the case of abrupt changes in scene elements or in the absence of ancillary data, the DS approach outperforms the other tested methods.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Water Desalination and Reuse Research Center (WDRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Yin G, Mariethoz G, Sun Y, McCabe MF (2017) A comparison of gap-filling approaches for Landsat-7 satellite data. International Journal of Remote Sensing 38: 6653–6679. Available: http://dx.doi.org/10.1080/01431161.2017.1363432.
Publisher:
Informa UK Limited
Journal:
International Journal of Remote Sensing
Issue Date:
10-Aug-2017
DOI:
10.1080/01431161.2017.1363432
Type:
Article
ISSN:
0143-1161; 1366-5901
Sponsors:
This work was supported by funding from King Abdullah University of Science and Technology (KAUST).
Additional Links:
http://www.tandfonline.com/doi/full/10.1080/01431161.2017.1363432
Appears in Collections:
Articles; Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorYin, Gaohongen
dc.contributor.authorMariethoz, Gregoireen
dc.contributor.authorSun, Yingen
dc.contributor.authorMcCabe, Matthewen
dc.date.accessioned2017-10-30T08:39:49Z-
dc.date.available2017-10-30T08:39:49Z-
dc.date.issued2017-08-10en
dc.identifier.citationYin G, Mariethoz G, Sun Y, McCabe MF (2017) A comparison of gap-filling approaches for Landsat-7 satellite data. International Journal of Remote Sensing 38: 6653–6679. Available: http://dx.doi.org/10.1080/01431161.2017.1363432.en
dc.identifier.issn0143-1161en
dc.identifier.issn1366-5901en
dc.identifier.doi10.1080/01431161.2017.1363432en
dc.identifier.urihttp://hdl.handle.net/10754/625993-
dc.description.abstractThe purpose of this study is to assess the relative performance of four different gap-filling approaches across a range of land-surface conditions, including both homogeneous and heterogeneous areas as well as in scenes with abrupt changes in landscape elements. The techniques considered in this study include: (1) Kriging and co-Kriging; (2) geostatistical neighbourhood similar pixel interpolator (GNSPI); (3) a weighted linear regression (WLR) algorithm; and (4) the direct sampling (DS) method. To examine the impact of image availability and the influence of temporal distance on the selection of input training data (i.e. time separating the training data from the gap-filled target image), input images acquired within the same season (temporally close) as well as in different seasons (temporally far) to the target image were examined, as was the case of using information only within the target image itself. Root mean square error (RMSE), mean spectral angle (MSA), and coefficient of determination (R-2) were used as the evaluation metrics to assess the prediction results. In addition, the overall accuracy (OA) and kappa coefficient (kappa) were used to assess a land-cover classification based on the gap-filled images. Results show that all of the gap-filling approaches provide satisfactory results for the homogeneous case, with R-2 > 0.93 for bands 1 and 2 in all cases and R-2 > 0.80 for bands 3 and 4 in most cases. For the heterogeneous example, GNSPI performs the best, with R-2 > 0.85 for all tested cases. WLR and GNSPI exhibit equivalent accuracy when a temporally close input image is used (i.e. WLR and GNSPI both have an R-2 equal to 0.89 for band 1). For the case of abrupt changes in scene elements or in the absence of ancillary data, the DS approach outperforms the other tested methods.en
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST).en
dc.publisherInforma UK Limiteden
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/01431161.2017.1363432en
dc.titleA comparison of gap-filling approaches for Landsat-7 satellite dataen
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalInternational Journal of Remote Sensingen
dc.contributor.institutionDepartment of Civil and Environmental Engineering, University of Maryland, College Park, MD, USAen
dc.contributor.institutionInstitute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerlanden
kaust.authorYin, Gaohongen
kaust.authorSun, Yingen
kaust.authorMcCabe, Matthewen
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