Full-Waveform LiDAR Point Cloud Land Cover Classification with Volumetric Texture Measures

Full-Waveform (FW) Light Detection and Ranging (LiDAR) systems record the complete waveforms of backscattered laser signals, thus providing greater potential for extracting additional features and deriving physical properties from reflected laser signals. This study explores the feasibility of extracting volumetric texture features from airborne FW LiDAR point cloud data along with echo-based LiDAR features to improve land-cover classification. A second derivative algorithm is used to detect signal echoes and extract single- and multi-echo features from FW LiDAR data derived from Gaussian fitting function. The dense point clouds are further regularized to construct a data cube for volumetric texture extractions using 3D-GLCM (Gray Level Co-occurrence Matrix) and Gray Level Co-occurrence Tensor Field (GLCTF) algorithms coupled with second and third order texture descriptors. Different feature combinations of traditional and echo-based LiDAR features and texture measures are collected for supervised land-cover classification using a Random Forests classifier. The experimental results indicate that the echo-based features may be useful for distinguishing general land-cover types with acceptable accuracy but may not be adequate for detailed classifications, such as discriminating different vegetation cover types. Incorporating volumetric texture features can improve the classification of relatively more detailed land-cover types with an approximate 10 and 14% increase in the overall accuracy and Kappa coefficient, respectively.