Brown, K. M. and Foody, Giles M. and Atkinson, Peter M. (2007) Modelling geometric and misregistration error in airborne sensor data to enhance change detection. International Journal of Remote Sensing, 28 (12). pp. 2857-2879. ISSN 0143-1161
Full text not available from this repository.Abstract
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 r 2 = 0.651; p = 0.017). 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.