Abstract
Abstract. Full-waveform LiDAR is an active technology of photogrammetry and remote sensing. It provides more detailed information about objects along the path of a laser pulse than discrete-return topographic LiDAR. The point cloud and waveform information with high quality can be obtained by waveform decomposition, which could make contributions to accurate filtering. The surface fitting filtering method with waveform information is proposed to present such advantage. Firstly, discrete point cloud and waveform parameters are resolved by global convergent Levenberg Marquardt decomposition. Secondly, the ground seed points are selected, of which the abnormal ones are detected by waveform parameters and robust estimation. Thirdly, the terrain surface is fitted and the height difference threshold is determined in consideration of window size and mean square error. Finally, the points are classified gradually with the rising of window size. The filtering process is finished until window size is larger than threshold. The waveform data in urban, farmland and mountain areas from “WATER (Watershed Allied Telemetry Experimental Research)” are selected for experiments. Results prove that compared with traditional method, the accuracy of point cloud filtering is further improved and the proposed method has highly practical value.
Highlights
In recent years, with the continuously emerge of commercial small-footprint airborne ground measuring device, a new airborne full-waveform LiDAR measurement system receives extensive attentions, which is capable of recording laser pulse (Lai Xudong, 2013; Qin Yuchu, 2011)
The basic idea of the method is to use decomposed waveform information and robust estimation theory to help the selection of seed points; and the surface fitting weights are determined by waveform parameters; the window size and mean square error of surface fitting are comprehensively considered to make height difference threshold determined adaptively
Full-waveform LiDAR is an active technology in the field of photogrammetry and remote sensing
Summary
With the continuously emerge of commercial small-footprint airborne ground measuring device, a new airborne full-waveform LiDAR measurement system receives extensive attentions, which is capable of recording laser pulse (Lai Xudong, 2013; Qin Yuchu, 2011). Current waveform data decomposition algorithms mainly include nonlinear least square method (Hofton M, 2000; Chanve A, 2007), expectation maximization (Persson A, 2005; Li Qi, 2008) and markov chain monte carlo algorithm (Hernandez-Martin S, 2007), in which the nonlinear least square method based on Levenberg Marquardt (LM) is widely applied This algorithm both has the advantages of gradient method and newton method, but has high requirements for initial value, which can run into local optimum; Filtering algorithms based on discrete point cloud geometric features can be divided into three main categories (Huang Xianfeng, 2009): morphological method (Qi C, 2007; Sui Lichun, 2010), method based on interpolation (Axelsson P, 2000; Kraus K, 1998) and method based on surface constraint (Su Wei, 2009). The double-blind peer-review was conducted on the basis of the full paper
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