The georeferencing errors of satellite data in remote sensing applications

Abstract

The change detection and analysis, as remote sensing application, is based on the multi-temporal and/or multi-sensors approaches. However, the accuracy of such change detection activities can be limited by several factors. A key variable that may reduce the accuracy of change detection is the misregistration error between the used images. By accounting for the spatial variation in geometric and misregistration errors there is the potential to reduce their effects during change detection, increasing the accuracy of land cover change mapping. The effect of misregistration on land cover mapping and change detection could be more accurately predicted and ultimately removed if this spatial variation in error was modeled. In this study, the estimation of the effect of misregistration on ASTER derived land cover types was attempt. The proposed methodology is based on the comparison of the regression correlation coefficients between two images derived either from one single band or from two bands. To check the level of correlation, a procedure of modifying the geometric position of one single band or of two different bands, using the same resolution or different resolutions was applied. In order to obtain this artificial degradation, a transformation on three directions: on x axis, on y axis and on both x and y axis of one image comparing with itself or with another was applied. The study was performed for different scales, different land cover types and different complexity to evaluate the most influencing factors. This approach allowed quantification of the inappropriate image georeferencing, as well as the quantitative estimation of the size of distortion of the final results, in case of comparison of images from different dates and different sensors.