Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

  • Authors:

  • Suresh K. Lodha;Edward J. Kreps;David P. Helmbold;Darren Fitzpatrick

  • Affiliations:

  • University of California, Santa Cruz, USA;University of California, Santa Cruz, USA;University of California, Santa Cruz, USA;University of California, Santa Cruz, USA

  • Venue:
  • 3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
  • Year:
  • 2006

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Abstract

We classify 3D aerial LiDAR scattered height data into buildings, trees, roads, and grass using the Support Vector Machine (SVM) algorithm. To do so we use five features: height, height variation, normal variation, LiDAR return intensity, and image intensity. We also use only LiDAR-derived features to organize the data into three classes (the road and grass classes are merged). We have implemented and experimented with several variations of the SVM algorithm with soft-margin classification to allow for the noise in the data. We have applied our results to classify aerial LiDAR data collected over approximately 8 square miles. We visualize the classification results along with the associated confidence using a variation of the SVM algorithm producing probabilistic classifications. We observe that the results are stable and robust. We compare the results against the ground truth and obtain higher than 90% accuracy and convincing visual results.