An optimized fuzzy K-means clustering method for automated rock discontinuities extraction from point clouds

Recently, non-contact measurement methods such as laser scanning, have gained popularity in collecting discontinuous data due to their ability to generate high-resolution point clouds containing detailed information about rock surface. However, quickly and accurately extracting discontinuities from massive point clouds faces challenges. In this study, we propose an optimization algorithm based on fuzzy clustering and region growth that enables swift extraction of discontinuity information from point clouds. The proposed method employed a composite indicator to evaluate the similarity and dissimilarity of the points in a clustering group basing on the membership function matrix and the optimal clustering number could be estimated without predefining. Additionally, region growing is difficult to deal with increasingly enormous point clouds, a faster way is estimating a possible range as a search radius to avoid meaningless time-consuming in region growing. Further, the proposed methodology was implemented in Matlab to extract discontinuities from high-resolution point cloud, includes data pre-processing, optimized fuzzy clustering, and optimized region growing. Finally, particular attention was given to the sensitivity of automatic extraction in point cloud resolution and cluster number, these parameters are always special in different objects. The results showed that the optimized method performed excellent in clustering without a priori assumption of cluster number, and provided optional range of resolutions without losses of accuracy to cater to diverse requirements. The proposed method gave a new way to estimate the optimal clustering number as same as manually separated without predefining, to collect all orientations of discontinuity quickly, and to meet different needs with appropriate resolution.