Details

Autor(en) / Beteiligte

• Lodha, S.K.
• Kreps, E.J.
• Helmbold, D.P.
• Fitzpatrick, D.

Titel

Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Ist Teil von

• Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06), 2006, p.567-574

Ort / Verlag

IEEE

Erscheinungsjahr

2006

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.