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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 - Quantifying the Effect of Registration Error on Spatio-Temporal Fusion
AU - Tang, Y.
AU - Wang, Q.
AU - Zhang, K.
AU - Atkinson, P.M.
PY - 2020/2/12
Y1 - 2020/2/12
N2 - It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resolution, especially for monitoring at global scales. Among the widely used global monitoring satellite sensors, Landsat data have a coarse temporal resolution, but fine spatial resolution, while moderate resolution imaging spectroradiometer (MODIS) data have fine temporal resolution, but coarse spatial resolution. One solution to this problem is to blend the two types of data using spatio-temporal fusion, creating images with both fine temporal and fine spatial resolution. However, reliable geometric registration of images acquired by different sensors is a prerequisite of spatio-temporal fusion. Due to the potentially large differences between the spatial resolutions of the images to be fused, the geometric registration process always contains some degree of uncertainty. This article analyzes quantitatively the influence of geometric registration error on spatio-temporal fusion. The relationship between registration error and the accuracy of fusion was investigated under the influence of different temporal distances between images, different spatial patterns within the images and using different methods (i.e., spatial and temporal adaptive reflectance fusion model (STARFM), and Fit-FC; two typical spatio-temporal fusion methods). The results show that registration error has a significant impact on the accuracy of spatio-temporal fusion; as the registration error increased, the accuracy decreased monotonically. The effect of registration error in a heterogeneous region was greater than that in a homogeneous region. Moreover, the accuracy of fusion was not dependent on the temporal distance between images to be fused, but rather on their statistical correlation. Finally, the Fit-FC method was found to be more accurate than the STARFM method, under all registration error scenarios. © 2008-2012 IEEE.
AB - It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resolution, especially for monitoring at global scales. Among the widely used global monitoring satellite sensors, Landsat data have a coarse temporal resolution, but fine spatial resolution, while moderate resolution imaging spectroradiometer (MODIS) data have fine temporal resolution, but coarse spatial resolution. One solution to this problem is to blend the two types of data using spatio-temporal fusion, creating images with both fine temporal and fine spatial resolution. However, reliable geometric registration of images acquired by different sensors is a prerequisite of spatio-temporal fusion. Due to the potentially large differences between the spatial resolutions of the images to be fused, the geometric registration process always contains some degree of uncertainty. This article analyzes quantitatively the influence of geometric registration error on spatio-temporal fusion. The relationship between registration error and the accuracy of fusion was investigated under the influence of different temporal distances between images, different spatial patterns within the images and using different methods (i.e., spatial and temporal adaptive reflectance fusion model (STARFM), and Fit-FC; two typical spatio-temporal fusion methods). The results show that registration error has a significant impact on the accuracy of spatio-temporal fusion; as the registration error increased, the accuracy decreased monotonically. The effect of registration error in a heterogeneous region was greater than that in a homogeneous region. Moreover, the accuracy of fusion was not dependent on the temporal distance between images to be fused, but rather on their statistical correlation. Finally, the Fit-FC method was found to be more accurate than the STARFM method, under all registration error scenarios. © 2008-2012 IEEE.
KW - Landsat
KW - MODIS
KW - registration error
KW - remote sensing data
KW - spatio-temporal fusion
KW - Errors
KW - Geometry
KW - Image resolution
KW - Radiometers
KW - Remote sensing
KW - LANDSAT
KW - Registration error
KW - Remote sensing data
KW - Spatio-temporal fusions
KW - Image fusion
KW - data set
KW - error analysis
KW - quantitative analysis
KW - remote sensing
KW - spatial resolution
KW - spatiotemporal analysis
U2 - 10.1109/JSTARS.2020.2965190
DO - 10.1109/JSTARS.2020.2965190
M3 - Journal article
VL - 13
SP - 487
EP - 503
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 -