A Decision Tree Classification Method Combining Intensity and RGB Value for LiDAR Data

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 569)

Abstract

Airborne light detection and ranging (LiDAR) has played an important role in obtaining spatial information. But most existing LiDAR data classification algorithms mainly based on elevation and need more manual participation. Compared to these algorithms, we emphasize the use of intensity, RGB and echo number, and put forward a decision tree classification method. Before using this method, the intensity value must be calibrated first, and the RGB usually assigned from orthophoto. Then the experiment show that classification work can be completed with high accuracy while reducing manual workload. In addition, it was found intensity information is useful in target detection.

Keywords

Classification Intensity RGB Echo number Decision tree 
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Notes

Acknowledgments

This work was supported by National High Technology Research and Development (863) Program (2012AA121304).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.National Key Laboratory of Remote Sensing Information and Image Analysis TechniqueBeijing Research Institute of Uranium GeologyBeijingChina

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