OriFlexClust denoising method for shallow water ATL03 single-photon point clouds

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Abstract Conventional clustering algorithms suffer from misclassification problems caused by uneven density and poor initial parameter choices. This paper introduces an adaptive denoising algorithm called OriFlexClust, based on directional angle continuity. Experimental results demonstrate that the OriFlexClust algorithm outperforms other clustering algorithms, effectively addressing the issues of parameter selection and uneven data density. It enhances denoising accuracy and processing speed. Therefore, the OriFlexClust algorithm proposed in this paper is more superior.