A Multiscale Erosion Operator for Discriminating Ground Points in LiDAR Point Clouds
Digital terrain models (DTM) are basic products required for a number of applications and decision making processes. Nowadays, high spatial-resolution DTMs are primarily produced through airborne laser scanners (ALS). However, the ALS does not directly deliver DTMs but a dense cloud of 3-d points that embeds both terrain elevation and height of natural and human-made features. Such a point cloud is generally rasterized and referred to as the digital surface model (DSM). The discrimination of aboveground objects from terrain, also termed ground filtering, is a basic processing step that has proved especially difficult for large areas of complex terrain characteristics. This paper presents the development of a multiscale erosion operator for removing aboveground features in the DSM, thus producing a surface that is close to the DTM. Such an approximation was used to separate ground from non-ground points in the original point-cloud and the discrimination accuracy was assessed using publicly available data. Results indicated an improvement over a previously published method.
KeywordsRemote sensing LiDAR Ground filtering multiscale Hermite transform
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