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Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method

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Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method. / Wang, L.; Wang, X.; Wang, Q.; Atkinson, P.M.

In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13, 26.10.2020, p. 6291-6307.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Wang, L, Wang, X, Wang, Q & Atkinson, PM 2020, 'Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6291-6307. https://doi.org/10.1109/JSTARS.2020.3030122

APA

Wang, L., Wang, X., Wang, Q., & Atkinson, P. M. (2020). Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6291-6307. https://doi.org/10.1109/JSTARS.2020.3030122

Vancouver

Wang L, Wang X, Wang Q, Atkinson PM. Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020 Oct 26;13:6291-6307. https://doi.org/10.1109/JSTARS.2020.3030122

Author

Wang, L. ; Wang, X. ; Wang, Q. ; Atkinson, P.M. / Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020 ; Vol. 13. pp. 6291-6307.

Bibtex

@article{8ba5ace027814d42b5744e2b7d8ec4d1,
title = "Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method",
abstract = "Spatio-temporal fusion is a common approach in remote sensing, used to create time-series image data with both fine spatial and temporal resolutions. However, geometric registration error, which is a common problem in remote sensing relative to the ground reference, is a particular problem for multiresolution remote sensing data, especially for images with very different spatial resolutions (e.g., Landsat and MODIS images). Registration error can, thus, have a significant impact on the accuracy of spatio-temporal fusion. To the best of our knowledge, however, almost no effective solutions have been provided to-date to cope with this important issue. This article demonstrates the robustness to registration error of the existing SParse representation-based spatio-temporal reflectance fusion model (SPSTFM). Different to conventional methods that are performed on a per-pixel basis, SPSTFM utilizes image patches as the basic unit. We demonstrate theoretically that the effect of registration error on patch-based methods is smaller than for pixel-based methods. Experimental results show that SPSTFM is highly robust to registration error and is far more accurate under various registration errors relative to pixel-based methods. The advantage is shown to be greater for heterogeneous regions than for homogeneous regions, and is large for the fusion of normalized difference vegetation index data. SPSTFM, thus, offers the remote sensing community a crucial tool to overcome one of the longest standing challenges to the effective fusion of remote sensing image time-series. {\textcopyright} 2008-2012 IEEE.",
keywords = "Downscaling, Landsat, moderate resolution imaging spectroradiometer (MODIS), registration error, sparse representation based spatio-temporal reflectance fusion model (SPSTFM), spatio-temporal fusion, Errors, Image fusion, Pixels, Time series, Geometric registrations, Multi-resolution remote sensing, Normalized difference vegetation index datum, Remote sensing images, Sparse representation, Spatial and temporal resolutions, Spatio-temporal fusions, Time-series image datum, Remote sensing, downscaling, error analysis, MODIS, NDVI, pixel, remote sensing, spatial resolution, spatiotemporal analysis",
author = "L. Wang and X. Wang and Q. Wang and P.M. Atkinson",
year = "2020",
month = oct,
day = "26",
doi = "10.1109/JSTARS.2020.3030122",
language = "English",
volume = "13",
pages = "6291--6307",
journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
issn = "1939-1404",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method

AU - Wang, L.

AU - Wang, X.

AU - Wang, Q.

AU - Atkinson, P.M.

PY - 2020/10/26

Y1 - 2020/10/26

N2 - Spatio-temporal fusion is a common approach in remote sensing, used to create time-series image data with both fine spatial and temporal resolutions. However, geometric registration error, which is a common problem in remote sensing relative to the ground reference, is a particular problem for multiresolution remote sensing data, especially for images with very different spatial resolutions (e.g., Landsat and MODIS images). Registration error can, thus, have a significant impact on the accuracy of spatio-temporal fusion. To the best of our knowledge, however, almost no effective solutions have been provided to-date to cope with this important issue. This article demonstrates the robustness to registration error of the existing SParse representation-based spatio-temporal reflectance fusion model (SPSTFM). Different to conventional methods that are performed on a per-pixel basis, SPSTFM utilizes image patches as the basic unit. We demonstrate theoretically that the effect of registration error on patch-based methods is smaller than for pixel-based methods. Experimental results show that SPSTFM is highly robust to registration error and is far more accurate under various registration errors relative to pixel-based methods. The advantage is shown to be greater for heterogeneous regions than for homogeneous regions, and is large for the fusion of normalized difference vegetation index data. SPSTFM, thus, offers the remote sensing community a crucial tool to overcome one of the longest standing challenges to the effective fusion of remote sensing image time-series. © 2008-2012 IEEE.

AB - Spatio-temporal fusion is a common approach in remote sensing, used to create time-series image data with both fine spatial and temporal resolutions. However, geometric registration error, which is a common problem in remote sensing relative to the ground reference, is a particular problem for multiresolution remote sensing data, especially for images with very different spatial resolutions (e.g., Landsat and MODIS images). Registration error can, thus, have a significant impact on the accuracy of spatio-temporal fusion. To the best of our knowledge, however, almost no effective solutions have been provided to-date to cope with this important issue. This article demonstrates the robustness to registration error of the existing SParse representation-based spatio-temporal reflectance fusion model (SPSTFM). Different to conventional methods that are performed on a per-pixel basis, SPSTFM utilizes image patches as the basic unit. We demonstrate theoretically that the effect of registration error on patch-based methods is smaller than for pixel-based methods. Experimental results show that SPSTFM is highly robust to registration error and is far more accurate under various registration errors relative to pixel-based methods. The advantage is shown to be greater for heterogeneous regions than for homogeneous regions, and is large for the fusion of normalized difference vegetation index data. SPSTFM, thus, offers the remote sensing community a crucial tool to overcome one of the longest standing challenges to the effective fusion of remote sensing image time-series. © 2008-2012 IEEE.

KW - Downscaling

KW - Landsat

KW - moderate resolution imaging spectroradiometer (MODIS)

KW - registration error

KW - sparse representation based spatio-temporal reflectance fusion model (SPSTFM)

KW - spatio-temporal fusion

KW - Errors

KW - Image fusion

KW - Pixels

KW - Time series

KW - Geometric registrations

KW - Multi-resolution remote sensing

KW - Normalized difference vegetation index datum

KW - Remote sensing images

KW - Sparse representation

KW - Spatial and temporal resolutions

KW - Spatio-temporal fusions

KW - Time-series image datum

KW - Remote sensing

KW - downscaling

KW - error analysis

KW - MODIS

KW - NDVI

KW - pixel

KW - remote sensing

KW - spatial resolution

KW - spatiotemporal analysis

U2 - 10.1109/JSTARS.2020.3030122

DO - 10.1109/JSTARS.2020.3030122

M3 - Journal article

VL - 13

SP - 6291

EP - 6307

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

ER -