Stepwise decomposition and relative radiometric normalization for small footprint LiDAR waveform

Abstract

As an advanced generation instrument of earth observation, small footprint full waveform light detection and ranging (LiDAR) technology has been widely used in the past few years. Decomposition and radiative correction is an important step in waveform data processing, it influences the accuracy of both information extraction and further applications. Based on a stepwise strategy, this study adopts Gaussian mixture model to approximate the LiDAR waveform. In addition to waveform decomposition, a relative correction model is proposed in this paper, the model considers the transmit pulses as well as the different of the travel path for implementing LiDAR waveform relative correction. Validation of the stepwise decomposition and relative correction model are carried out on LiDAR waveform acquired over Zhangye, China. The results indicate that stepwise decomposition identified the number of peaks in LiDAR waveforms, center position and width of each peak well. The relative radiometric correction also improves the similarity of waveforms which acquired at the same target.