Brown, K.M., Foody, G.M. and Atkinson, P.M.
Modelling geometric and misregistration error in airborne sensor data to enhance change detection
International Journal of Remote Sensing, 28, (12), . (doi:10.1080/01431160600981533).
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One of the major goals of remote sensing is to carry out monitoring programmes
such as land-cover change detection. However, the accuracy of such change
detection activities can be limited by several factors. A key variable that can limit
the accuracy of change detection is the misregistration error between the images
used. Although the impacts of misregistration on change detection have been
considered in various studies, a single global value for misregistration has
typically been applied across the whole scene. The effect of misregistration,
however, varies spatially and its effects on change detection could be more
accurately predicted and ultimately removed if this spatial variation in error were
modelled. The current study aimed to develop a model that described the spatial
variation of misregistration for airborne image data. As misregistration is a
function of geometric error, the geometric errors associated with the airborne
data were modelled and this geometric error model was used to derive a model of
misregistration. The impacts of various navigational variables on the accuracy of
the automated geocorrection of compact airborne spectrographic imager (CASI)
data were evaluated. A significant relationship was found between geometric
error and angular acceleration (adjusted r250.651; p50.017). The relationship
between geometric error and angular acceleration together with a model of
orthometric errors was used to derive an error model that described the spatial
variation in geometric errors associated with the automated geocorrection of the
CASI data. This model gave a probabilistic description of the spatial variation in
geometric error. From the geometric error model, a model of misregistration
between CASI images from two times was derived. This model was tested using
data from an urban test site and a significant correlation, at 95% confidence, was
found between predicted and measured misregistration. The models derived
could be used in change detection, potentially reducing the impact of geometric
errors and so misregistration in airborne sensor data, which is a major limitation
in the use of remote sensing for environmental monitoring.
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