A method of classification for airborne full waveform LiDAR data based on random forest

作者：Fan Shijun; Zhang Aiwu^*; Hu Shaoxin; Sun Weidong

来源：Chinese Journal of Lasers, 2013, 40(9): 0914001.

DOI：10.3788/CJL201340.0914001

• Comparative analysis
• Feature vectors
• Full-waveform data
• Full-waveforms
• Good stability
• Random forest classification
• Random forest methods
• Urban classification

Aiming at airborne full waveform data classification, the paper proposes a random forest method based on point cloud classification algorithm. The amplitude of the fullwaveform echo and echo times, as well as the echo width are extracted. Extract and select the features using the method proposed in this paper to build a feature vector. Using the random forest method, the laser point clouds are divided into three types of vegetation, ground and building. By comparative analysis of random forests method and the support vector machine, the results present that the extracted features show good stability and efficiency, and the random forest classification method can achieve good classification effect in the urban classification applications.

• 出版日期2013
• 单位首都师范大学