Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Chapel Hill, NC, 2006, Pages 567-574.

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support vector machinesimage intensityaerial lidar scattered heightprobabilistic classificationlidar return intensityMore(21+)

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...More

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