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3DPVT
2006
IEEE

Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

9 years 5 months ago
Aerial LiDAR Data Classification Using Support Vector Machines (SVM)
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 LiDARderived 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.
Suresh K. Lodha, Edward J. Kreps, David P. Helmbol
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where 3DPVT
Authors Suresh K. Lodha, Edward J. Kreps, David P. Helmbold, Darren N. Fitzpatrick
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