Automated correction of surface obstruction errors in digital surface models using off-the-shelf image processing

Abstract

Airborne topographic data collection requires removal of errors that arise due to surface features that obstruct the ground from the sensor. Typically, this has been based on manual correction and/or automated filtering. To some degree, the latter has provided a method for identifying and removing unwanted surface obstructions in large topographic data-sets. However, the algorithms used are unintelligent in that they cannot reliably differentiate between the various types of obstructions and the ground. If coincident optical support imagery is available, the use of intelligent correction routines becomes possible. This paper describes an automated approach for removing obstruction errors using optical support imagery and simple image processing routines. Orthorectification and classification of support imagery enable obstruction errors to be identified in the digital surface model (DSM) and corrected intelligently to produce a digital terrain model (DTM). The results show that support imagery can be used with basic image processing routines to remove obstructions intelligently and automatically from large topographic data-sets. Since the approach can differentiate between types of obstructions, the removal of each type of error can be customised, making this a very flexible approach to topographic data correction.