Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 447-452, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-447-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
09 Jun 2016
LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES
M. Zhou, C. R. Li, L. Ma, and H. C. Guan Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China
Keywords: LiDAR, Support Vector Machines (SVM), Full-Waveform, Land Cover Classification, Waveform decomposition, Feature extraction Abstract. In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.
Conference paper (PDF, 1232 KB)


Citation: Zhou, M., Li, C. R., Ma, L., and Guan, H. C.: LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 447-452, https://doi.org/10.5194/isprs-archives-XLI-B3-447-2016, 2016.

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