Visualizing Positional Uncertainties of Geometric Corrected Remote Sensing Images
Carlos Vieira +, Giuliano Marotta, Dalto Rodrigues and Rafael Andrade
Federal University of Viçosa – Civil Engineering Department – DEC
Surveying Engineering Sector, Viçosa – MG – Brazil CEP 36570-000
Abstract. A new method to evaluate and to visualize positional uncertainties that occur through the geometric correction process of remote sensing images is successfully presented. Five different transformation methods are described. Results show that the best RMS error was obtained by 3D the projective modified model and the worst one was obtained by the 2D affine model. The 3D projective model
and 3D projective modified one were very similar in performance. These results also point out the importance in choose a better transformation model in order to perform the geometric corrections in remote sensed data and emphasize the importance of select a number of GCP spread all over the study area. Moreover, using the proposed positional error map, it is possible to evaluate every observation, with its precisions, offering high confidence in the transformed image coordinates. It is also important to mention that the use of the variance propagation rules allows analyzing the residual uncertainties of the transformation parameters spatially in the entire image.
Keywords: positional accuracy, error propagation, geometric correction, least mean square, visualizing uncertainty.
In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).