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.