Positional Accuracy Analysis of Satellite Imagery by Circular Statistics

The proposed method in this paper uses circular statistics for the analysis of errors in the positional accuracy of geometric corrections satellite images using Independent Check Lines (ICL) instead of Independent Check Points (ICP). Circular statistics has been preferred because of the vectorial nature of the spatial error. A study case has been presented and discussed in detail. From the TERRA-ASTER images of Extremadura area (Spain), the Ground Control Point (GCP), ICP, and ICL data were acquired using differential GPS through field survey, and the planimetric positional accuracy was analyzed by both the conventional method (using ICP) and the proposed method (using ICL). Comparing conventional and proposed methods, the results indicated that modulus statistics are similar (e.g., RMSE of Geometric Correction 1 were 17.5 for the conventional method and 17.2 m for proposed method). But as additional results, azimuthal component statistics was calculated (e.g., mean direction: 247.2 ̊in Geometric Correction 1), and several tests were made which showed the error distribution are not uniform and normal. Introduction Geometric correction and georeferencing are the essential tasks in utilizing the spatial imagery for extracting geospatial information. Obviously, the quality of spatial data depends on the positional accuracy of the georeferenced images, which must be carefully tested. Moreover, the positional and thematic accuracy of derived maps, as land-use and land-cover, that are generated from multispectral space imagery is also usually represented in terms of accuracy, which are commonly calculated from an error matrix, or confusion matrix (Congalton and Green, 2009). Owing to the importance of positional accuracy, some official committees published guidelines for quality checking of the data, which describe the checking procedures, the minimum number of independent check points (ICP), and the recommended statistics. By applying to their satellite and aerial imagery studies, some authors have tried to resolve the problem of errors in positional accuracy (Höhle, 1996; Kay et al., 2003; Kardoulas et al., 1996). The most commonly referenced guide is the Geographic Information Framework Data Content Standard (FGDC, 2008) from the US Federal Geographic Data Committee (FGDC) which states that the accuracy of the new or revised spatial PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Novembe r 201