Evaluation of Potential of Blunder Detection Techniques in Digital Terrain Modelling

Nowadays, digital terrain models (DTM) are an important source of spatial data for various applications in many scientific and commercial disciplines. Therefore, special attention is given to their main characteristic ‐ accuracy. These models are infected with various types of errors in the process of sampling, measurement and reconstruction. These errors are divided in to three groups: random, systematic and gross errors. As it is well known, the source data for DEM creation contributes a large amount of errors, including gross errors (blunders), to the final product, which are unacceptable for a practical project. Therefore, the detection and deletion of gross error from DTM data has been becoming a great concern in geospatial data analysis. Most of existing approaches are based on statistical tests and present considerable problems for isolating observations and avoiding their influence. This paper presents an algorithm based on robust estimation with IRLS. Also, the application of robust methods to digital terrain modeling is analyzed versus the classical least-squares approach. Entire dataset is divided into some separate patches. In each patch a bilinear surface is fit to fully surrounded points and the residual for each point is estimated. By the use of robust estimation, it is tried to minimize the sum of squared residuals in order to detect points with gross error. The results showed that the proposed method provides a maximum-resistance solution and therefore the capability of identifying blunders.