T photon point cloud denoising method based on a Gaussian mixture model

Abstract

The invention discloses a photon point cloud denoising algorithm based on a Gaussian mixture model, and the method comprises the steps: firstly building a local elevation frequency histogram based onthe original photon counting laser radar data, roughly determining the position of a signal photon, and removing most photon noise; extracting characteristic parameters based on the photon point cloudafter coarse denoising, and establishing a Gaussian mixture model; using k-mean algorithm to realize the initial estimation of mixed Gaussian model parameters ; and finally, carrying out parameter optimization by using an EM algorithm to realize accurate separation of noise photons and signal photons. According to the method, the problems of nonuniform distribution of photon point cloud, incomplete boundary data and the like can be solved, the method has the characteristics of no need of parameter setting, high adaptive degree and the like, and the automation degree and precision of photon point cloud denoising are improved.

Description

technical field [0001] The invention relates to a photon point cloud denoising method, in particular to a photon point cloud denoising method based on a mixed Gaussian model. Background technique [0002] Spaceborne lidar has been widely used in many fields such as global terrain mapping, glacier change monitoring, global forest tree height and biomass mapping. The new generation of spaceborne lidar will adopt photon counting technology. As a new type of earth observation technology, photon counting lidar can obtain more sampling data to provide more detailed surface information. However, the emission pulse of photon counting lidar is a weak signal, which is greatly affected by noise (solar background noise, system noise, atmospheric scattering noise), and the spatial distribution of photon noise is random and extensive, which seriously affects the subsequent application of photon counting lidar data. , how to effectively remove photon noise has become a major challenge in ...

AI Technical Summary

Problems solved by technology

These existing methods can remove photon noise to a certain extent, but they all have defects. For example, the influence of factors such as uneven distribution of photon point cloud and incomplete boundary data on the denoising effect have not been considered, and no effective measures have been taken to eliminate near-ground noise. and photon noise near the top of the canopy

In addition, these existing photonic point cloud denoising methods all rely on experience to set thresholds or parameters, which are not universally applicable.

[0004] In summary, photon counting, as a new type of earth observation technology, will be widely used in many fields such as global change, forestry, and surveying and mapping. However, its application is restricted by photon point cloud denoising methods.

The existing photon point cloud denoising method is not yet mature, so it is urgent to propose an effective photon point cloud denoising method

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