Research on classification of LiDAR images derived from waveform decomposition over a suburban area

Light detection and ranging (LiDAR), as an active remote sensing technology, is characterized by providing high-precision geographical location information. In this study, we further explored its capability in image classification over a suburban area. Firstly, full waveforms of small footprint airborne LiDAR were decomposed into discrete point clouds. During the decomposition, six parameters describing the physical interaction between laser pulse and the targets were calculated. They are amplitude, pulse width, central position, range, backscatter cross-section and backscatter coefficient. Secondly, the point clouds were interpolated into raster. Correspondingly, six high spatial resolution images (0.5 m) were produced. Three classification models namely decision tree (DT), maximum likelihood (ML) and support vector machine (SVM) were established based on these images. The objects of interest were classified into buildings, trees, bare soil and crop land. Results showed that all these three models yielded high overall accuracy and kappa coefficient. SVM performed the best with the highest overall accuracy (87.85%) and kappa coefficient (83.29%). Therefore, we came to conclude that classification models can also achieve satisfactory classification accuracy on LiDAR images as they did on common remote-sensed images. In addition, our study proved that physical information derived from waveform LiDAR showed good potential in classification.