Efficient vegetation filtering method using K-means clustering algorithm: case study of high steep rock slope - Guan, Shenggong; Yuan, Mingsong; Liu, Junyang; Luo, Xun; Wu, Faquan; Shi, Zhenming; Zheng, Hongchao (Frontiers in Earth Science, 2025)
Vegetation obscures critical rock‐mass features on steep slopes, degrading the reliability of structural surface interpretation from point clouds. We propose a fast vegetation‐filtering approach tailored to high‐steep, vegetated rock slopes. The method aims to suppress vegetation noise while preserving terrain points essential for structural analysis. We first perform dual-channel dimensionality reduction by combining Principal Component Analysis (PCA) on spatial coordinates with the Red–Green Difference Index (RGDI) from RGB values, then apply K-means clustering for segmentation. A hierarchical grid plus local plane fitting is used to select vegetation seed points; distances to the fitted plane guide seed assignment and subsequent cluster-level filtering. To prevent over-filtering, a preservation mechanism based on the 3σ rule retains 5%–32% of points near the seed-point distance threshold. The method was evaluated on a rugged, vegetation-covered slope at Tiantai Mountain (Zhejiang, China) acquired with a Topcon GLS-2000 (384,663 points). The effectiveness of the filtering results is evaluated using class error, Ie, IIe and total error Ae. Comparing with other algorithms, it is found that the class error, Ie is 7.79%, the class error IIe is 4.34%, and the total result is limited to 6.53%. By jointly leveraging spatial and spectral cues with grid-wise plane fitting and a preservation guarantee, the approach effectively suppresses vegetation noise while retaining terrain detail needed for downstream tasks (e.g., structural plane interpretation). The results indicate improved filtering accuracy and robustness for high-steep terrains relative to traditional methods.