Photon counting laser radar data denoising method

Abstract

The invention provides a photon counting laser radar data denoising method, and belongs to the technical field of laser radar data processing. According to the photon counting laser radar data denoising method, original photon data is subjected to quadtree space segmentation, a space formed by the original photon data is converted into a quadtree structure, then the isolation depth of each photon in the original photon data is calculated, and the photons with the isolation depth smaller than a set depth threshold value are marked as noise photons and removed, so that signal photons are obtained from the original photon data. The photon counting laser radar data denoising method can extract the signal photons accurately from the original data with different signal-to-noise ratios, so that the noise in the photon counting laser radar data can be accurately and efficiently removed.

Description

technical field [0001] The invention relates to a photon counting lidar data denoising method, which belongs to the technical field of laser radar data processing. Background technique [0002] Remote sensing is an all-weather and all-weather means of earth observation. Spaceborne laser altimeters have been proven to have unique advantages in obtaining high-precision forest vertical structure and ground elevation. ICESat-2 (Ice Cloud and Land Elevation Satellite-2) is a new generation of laser altimeter launched by NASA in September 2018. It is also the world's first spaceborne photon counting laser altimeter. Its detection performance has reached a new level. , the ATLAS (theAdvanced Topographic Laser Altimeter System) equipped with it is loaded with 6 beams of 532nm blue-green lasers, and measures the ground at a repetition rate of 10kHz. Before the official launch of ICESat-2, NASA conducted multiple airborne experiments, including MABEL and MATLAS, to obtain a large amo...

AI Technical Summary

Problems solved by technology

For example, Zhang and Kerekes proposed a DBSCAN clustering algorithm using a horizontal elliptical search area. Since the spatial distribution of photons is related to the terrain, this algorithm can achieve good results in plains, but the denoising results in mountainous areas are not good; Xie Feng et al. proposed an improved DBSCAN algorithm by adding a direction parameter in the horizontal ellipse search area, which enhanced the robustness of the terrain. The improved DBSCAN algorithm is most widely used in spaceborne photon counting lidar data denoising. However, the search area The size and proportion of the DBSCAN clustering algorithm have a great influence on the denoising results of the DBSCAN clustering algorithm, and it is difficult to accurately calculate these parameters; Bowei Chen et al. proposed an improved LOF (local outlier factor) algorithm using an elliptical search area, and also It is found that the shape of the search area has a great influence on the denoising results. Although the algorithm proves that the horizontal ellipse search area has the best effect, it does not give the calculation method of the input parameters; Xiaoxiao Zhu proposed an OPTICS (ordering points To identify the clustering structure) clustering algorithm and use the horizontal ellipse search area to improve, this algorithm reduces the sensitivity to clustering parameters, but the radius of the search area still needs to be given artificially

[0004] To sum up, the denoising effect of the current denoising algorithm based on photon density is greatly affected by the input parameters, and it is not robust to the input parameters. If the input parameters are set accurately, a better denoising effect can be obtained. Inaccurate input parameter setting is not good for denoising effect. Since the input parameters are difficult to calculate accurately in practical applications, the denoising effect of current spaceborne photon counting lidar data cannot be guaranteed.

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