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Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscatteredsignal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems canprovide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground byemploying a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are themain inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed bycomparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionallythe transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions arethat from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS