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Evaluation of gap-filling methods for Landsat 7 ETM+ SLC-Off image for LULC classification in a heterogeneous landscape of West Africa

Asare, Yaw Mensah and Forkuo, Eric and Forkuor, Gerald and Thiel, Michael (2019) Evaluation of gap-filling methods for Landsat 7 ETM+ SLC-Off image for LULC classification in a heterogeneous landscape of West Africa. International Journal of Remote Sensing, 41 (7), pp. 2544-2564. Taylor & Francis. doi: 10.1080/01431161.2019.1693076. ISSN 0143-1161.

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Official URL: https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1693076


The Landsat mission which has existed over 5 decades has remained on the forefront of providing consistent moderate spatial and temporal resolution optical images of the earth. The failure of the scan line corrector (SLC) on-board the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in May 2003 has permanently resulted in data gaps on each Landsat 7 scene. Due to the obvious negative impacts on the image usability, a number of methods have been developed to fill the no-data areas in the image. This study assessed the performance of four Landsat 7 ETM+ (LS7) SLC-off gap-filling methods in a highly heterogeneous landscape of West Africa for two different seasons (dry and rainy). The methods considered are: (1) Weighted Linear Regression (WLR) integrated with Laplacian Prior Regularization Method (LPRM), (2) Localised Linear Histogram Matching (LLHM), (3) Neighbourhood Similar Pixel Interpolator (NSPI) and (4) Geostatistical Neighbourhood Similar Pixel Interpolator (GNSPI). All the images used were LS7 SLC-off images, temporally close and from the same season for each set of time step. Visual comparison, mean and standard deviations of the histograms of all bands of only the filled areas were used to assess the results. Additionally, overall accuracy (OA), kappa coefficient (κ) and balanced accuracy (BA) per class were used to evaluate a LULC classification based on the gap-filled images. Visually, all the four methods were able to completely fill the gaps in the LS7 SLC-off image. They all look similar and spatially continuous with no anomalies or artefacts on them. The histograms from each band for only the filled areas for all the four methods also gave similar means and standard deviations in most cases. All the four gap-filling methods provided satisfactory results (OA >96% and κ > 0.937 in all methods for images in the dry season and OA >93% and κ > 0.877 for the image in the rainy season) in the land cover classification considering the complexity of the study area. But the GNSPI was superiority in all cases with the highest OA of 97.1% and κ of 0.947 in the dry season and OA of 94.6% and κ of 0.899 in the rainy season. This implies that the GNSPI is more robust in gap-filling of LS7 SLC-off images than the other three methods in a heterogeneous landscape of West Africa regardless of the season. This study suggest that gap-filling of LS7 SLC-off images will help to increase the number of Landsat images needed to build a time series data for a data scarce region such as West Africa.

Item URL in elib:https://elib.dlr.de/132490/
Document Type:Article
Title:Evaluation of gap-filling methods for Landsat 7 ETM+ SLC-Off image for LULC classification in a heterogeneous landscape of West Africa
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Journal or Publication Title:International Journal of Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 2544-2564
Publisher:Taylor & Francis
Keywords:Landsat 7 ETM+ SLC-off image, Gap-filling methods, West Africa, heterogeneous landscape, gap-filled image
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Geoscientific remote sensing and GIS methods
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:09 Dec 2019 13:36
Last Modified:09 Dec 2019 13:36

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