Correction of drone LiDAR point cloud intensity for salt marsh wetlands based on BP neural network
Aiming at the intricate relationship between intensity and the influencing factors complicates the accurate intensity correction using conventional mathematical and physical models,the correlation between the target's reflective characteristics,the instrumental photoelectric conversion principles and the spatial geometry(distance and incidence angle)of the salt marsh wetland was leveraged in this paper,then a novel method of the drone light detection and ranging(LiDAR)intensity correction for salt marsh wetlands was proposd by employing a back propagation(BP)neural network architecture with two hidden layers representing diffuse and specular reflection.In the proposed method,Bayesian optimization was employed to identify the optimal hyperparameters for the BP neural network,thereby establishing a mapping relationship between spatial geometry and intensity data.The results demonstrated a correction accuracy improvement of approximately 4.58％and 25％over the existing improved normalized correction model and Phong correction model,respectively.Furthermore,the classification accuracy by the corrected intensity data of the proposed method were increased by 6.36％and 2.11％compared to those by the two exisiting methodoriginal intensity data.Notably,this method obviated the need for complex indoor and outdoor calibration experiments and does not require consideration of the internal photoelectric conversion mechanism or the laser radiation transmission process.Consequently,this technique offered a reliable data foundation for the precise modeling of multi-platform and multi-echo LiDAR intensity data,facilitating the extraction of feature information from salt marsh wetlands in complex environments.
drone LiDARsalt marsh wetlandecho intensityintensity correctionneutral work
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