LAND COVERS CLASSIFICATION BASED ON RANDOM FOREST METHOD USING FEATURES FROM FULL-WAVEFORM LIDAR DATA
- Key Laboratory of Quantitative Remote Sensing Information Technology Academy of Opto-electronics, Chinese Academy of Sciences, 100094 Beijing, China
Keywords: Random forest (RF), Full-Waveform LiDAR, Waveform decomposition, Skewness, Kurtosis, Land Covers Classification
Abstract. In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.