Waveform decomposition and feature extraction of airborne LiDAR bathymetry

Abstract

To develop the data mining capability of airborne LiDAR bathymetry (ALB) waveform information, the waveform decomposition and feature extraction algorithm of airborne LiDAR bathymetry is proposed in this paper. This method uses the “separate” and “combine” two-step optimized waveform decomposition algorithm to decompose the bottom contribution waveform and extract 7 waveform feature parameters. To verify the effectiveness of this algorithm, it is applied to Yuanzhi Island ALB data. The experimental results showed that the average root mean square error (RMSE) of waveform decomposition is 11.75; the average coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) is 0.983, and the extraction time for every 500 sets of data is only 0.24 s. After classing coral reefs using the extracted ALB waveform features, the overall accuracy of coral reef information extraction are 2.27%,3.84%, and 4.02% improved with K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) methods, respectively, compared with the previous waveform features.