LAUSR

Airborne laser bathymetry (ALB) systems with digital full-waveform signal collection can obtain the corresponding temporal positions from several backscattering surfaces by laser beam irradiation. This information can help describe the multielevation structures of the target and explore the echo signal attenuation response in different nonuniform mediums during laser propagation. Therefore, a full-waveform echo signal is quite practical for integrated water–land detection. However, the wavelength used in the ALB system is generally in the visible band range of 470–580 nm, and the received signal is constantly interfered with by many nontarget factors, such as imperfections in the receiving channel or the strong scattering from the transmission medium. The conventional processing method transforms nontarget interference into noise point cloud filtering or classification extraction, enabling the detection of a single surface or regular geometry. The accuracy of the identification and extraction for multielevation target surfaces echo signal is always reduced due to the significant noise signal intensity. We proposed a signal component detection method by constructing the echo signal feature functions and the conditional random field (CRF) model based on the full-waveform decomposition. The processing result for actual measurement data verified that the CRF strategy can effectively reduce the uncertainty of target surface detection. Compared with the single-beam echo sounder, the root-mean-squared errors of the elevation deviation underwater were reduced by 3.2 and 4.9 cm in the two different experimental areas.