Segmentation and Classification of Airborne Laser Scanner Data

Abstract

Various methods have been developed to measure the physical presence of objects in a landscape with high positional accuracy. A new method that has been gaining popularity is Airborne Laser Scanning (ALS). ALS works by scanning a landscape (the collection of ground, buildings, vegetation, etc.,) in multiple passes. In each scan pulses of laser light are emitted from an airborne platform and their return time is measured, thus enabling the range from the point of emission to the landscape to be determined. The product of airborne laser scanning is a cloud of points in 3D space. ALS is capable of delivering very dense and accurate point clouds of a landscape in a relatively short time. However, in spite of the ability to measure objects with high positional accuracy, the automatic detection and interpretation of individual objects in landscapes remains a challenge. An example of just such a challenge is the classification of point clouds produced by ALS. The classification of ALS point clouds consists firstly in the labeling of points as either object or bare earth. The labeled object points are then further labeled as either building or vegetation. As a measurement technique ALS holds great promise and motivated by the desire to promote it, research has been conducted here to automate the detection of bare earth, buildings and vegetation in ALS point clouds.