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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Spatial-Spectral Radial Basis Function-Based Interpolation for Landsat ETM+ SLC-Off Image Gap Filling
AU - Wang, Q.
AU - Wang, L.
AU - Li, Z.
AU - Tong, X.
AU - Atkinson, P.M.
N1 - ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2021/9/30
Y1 - 2021/9/30
N2 - The scan-line corrector (SLC) of the Landsat 7 ETM+ failed permanently in 2003, resulting in about 22% unscanned gap pixels in the SLC-off images, affecting greatly the utility of the ETM+ data. To address this issue, we propose a spatial-spectral radial basis function (SSRBF)-based interpolation method to fill gaps in SLC-off images. Different from the conventional spatial-only radial basis function (RBF) that has been widely used in other domains, SSRBF also integrates a spectral RBF to increase the accuracy of gap filling. Concurrently, global linear histogram matching is applied to alleviate the impact of potentially large differences between the known and SLC-off images in feature space, which is demonstrated mathematically in this article. SSRBF fully exploits information in the data themselves and is user-friendly. The experimental results on five groups of data sets covering different heterogeneous regions show that the proposed SSRBF method is an effective solution to gap filling, and it can produce more accurate results than six popular benchmark methods. CCBY
AB - The scan-line corrector (SLC) of the Landsat 7 ETM+ failed permanently in 2003, resulting in about 22% unscanned gap pixels in the SLC-off images, affecting greatly the utility of the ETM+ data. To address this issue, we propose a spatial-spectral radial basis function (SSRBF)-based interpolation method to fill gaps in SLC-off images. Different from the conventional spatial-only radial basis function (RBF) that has been widely used in other domains, SSRBF also integrates a spectral RBF to increase the accuracy of gap filling. Concurrently, global linear histogram matching is applied to alleviate the impact of potentially large differences between the known and SLC-off images in feature space, which is demonstrated mathematically in this article. SSRBF fully exploits information in the data themselves and is user-friendly. The experimental results on five groups of data sets covering different heterogeneous regions show that the proposed SSRBF method is an effective solution to gap filling, and it can produce more accurate results than six popular benchmark methods. CCBY
KW - Gap filling
KW - Landsat ETM+
KW - radial basis function (RBF) interpolation
KW - scan-line corrector (SLC)-off.
KW - Filling
KW - Interpolation
KW - Effective solution
KW - Heterogeneous region
KW - Histogram matching
KW - Interpolation method
KW - Radial Basis Function(RBF)
KW - Radial basis functions
KW - Scan line correctors
KW - User friendly
KW - Functions
U2 - 10.1109/TGRS.2020.3038878
DO - 10.1109/TGRS.2020.3038878
M3 - Journal article
VL - 59
SP - 7901
EP - 7917
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
IS - 9
ER -