Abstract
Abstract. Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.
Highlights
Airborne LiDAR has already proven to be a state-of-the-art technology for high resolution and highly accurate topographic data acquisition with active and direct determination of the earth surface elevation (Vosselman and Maas, 2010)
FWF data provide additional information which offers the opportunity to overcome many drawbacks of classical multi-echo LiDAR data on reflecting characteristics of the objects, which are relevant in urban classification
The classification methods applied reach from simple decision trees to support vector machines (SVM). (Ducic et al, 2006) applied a decision tree based on amplitude, pulse width, and the number of pulses attributes of full-waveform data in order to distinguish the vegetation points and non-vegetation points. (Rutzinger et al, 2008) used a decision tree based on the homogeneity of echo width to classify points from full-waveform ALS data to detect tall vegetation - trees and shrubs. (Mallet et al, 2008) used SVM to classify four main classes in urban area
Summary
Airborne LiDAR has already proven to be a state-of-the-art technology for high resolution and highly accurate topographic data acquisition with active and direct determination of the earth surface elevation (Vosselman and Maas, 2010). A higher number of detected echoes has been reported for FWF data in comparison to discrete return point clouds. These additional attributes were successfully used in classification (Alexander et al, 2010). (Mallet et al, 2008) used SVM to classify four main classes in urban area (e.g. buildings, vegetation, artificial ground, and natural ground). In these studies the parameters of the classification (threshold values, etc.) are set by expert knowledge or learned from training data. These values are optimal for the investigated data set
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