Improving the Elevation Accuracy of Digital Elevation Models: A Comparison of Some Error Detection Procedures

Abstract

The widespread availability of powerful desktop computers, easy‐to‐use software tools and geographic datasets have raised the quality problem of input data to be a crucial one. Even though accuracy has been a concern in every serious application, there are no general tools for its improvement. Some particular ones exist, however, and some results are presented here for a particular case of quantitative raster data: Digital Elevation Models (DEM). Two procedures designed to detect anomalous values (also named gross errors, outliers or blunders) in DEMs, but valid also for other quantitative raster datasets, were tested. A DEM with elevations varying from 181 to 1044 m derived from SPOT data has been used as a contaminated sample, while a manually derived DEM obtained from aerial photogrammetry was regarded as the ground truth to allow a direct performance comparison for the methods with real errors. It is assumed that a “better” value can be measured or obtained through some methodology once an outlier location is suggested. The options are different depending upon the user (DEM producers might go to the original data and make another reading, while end users might use interpolation). Both choices were considered in this experiment. Preliminary results show that for the available dataset, the accuracy might be improved to some extent with very little effort. Effort is defined here as the percentage of points suggested by the methodology in relation with its total number: thus 100 per cent effort implies that all points have been checked. The method proposed by López (1997) gave poor results, because it has been designed for errors with low spatial autocorrelation (which is not the case here). A modified version was then designed and compared with the method proposed by Felicísimo (1994). The three procedures can be applied both for error detection during DEM generation and by end users, and they might be of use for other quantitative raster data. The choice of the best methodology is different depending on the effort involved. The conclusions have been derived for a photogrammetrically obtained DEM; other production procedures might lead to different results.