SLOPE BASED FILTERING OF LASER ALTIMETRY DATA

TLDR

Laser altimetry is the leading technique for large‑scale height acquisition, yet converting raw measurements into digital elevation models remains challenging because ground returns must be separated from those reflected by buildings and vegetation. The paper proposes a novel filtering method for laser data. The method adapts a grey‑scale morphological erosion operator, deriving filter functions from height differences in a training set to either preserve terrain features or reduce classification errors. Experiments demonstrate that the error‑minimizing filter yields lower DEM errors, though performance degrades as point density decreases.

Abstract

Laser altimetry is becoming the prime method for large scale acquisition of height data. Although laser altimetry is full integrated into processes for the production of digital elevation models in different countries, the derivation of DEM's from the raw laser altimetry measurements still causes problems. In particular the laser pulses reflected on the ground surface need to be distinguished from those reflecting on buildings and vegetation. In this paper a new method is proposed for filtering laser data. This method is closely related to the erosion operator used for mathematical grey scale morphology. Based on height differences in a representative training dataset, filter functions are derived that either preserve important terrain characteristics or minimise the number of classification errors. In experiments it is shown that the latter filter causes smaller errors in the resulting digital elevation models. In general the performance of the filters deteriorates with a decreasing point density.