Separability Of Targets In Urban Areas Using Features From Full-Waveform Lidara Data

Geometric and radiometric attributes of targets are provided by full-waveform LiDAR data. However, the accuracy of such information depends largely on the adopted data processing method. In this study, the emphasis is on the retrieval of the temporal target cross-section by regularization methods, with the subsequent extraction of the backscattering cross-section (BCS) and backscatter coefficient (BC), the aim being to characterize different classes in an urban scene. In particular, a sparsity constraint regularization method has been investigated to provide a temporal target response with high resolution. The L-curve method is represented as a proper approach for estimation of the optimal regularization parameter, where a polynomial function is fitted to a group of discrete points associated with the corresponding values between the two terms in the objective function. The proposed methods have been tested with real full-waveform LiDAR data, demonstrating the capability of efficient separation of targets in the waveform signal.