Surface Extraction and Segmentation From 3-D Underwater Sub-Bottom Point Clouds Using Enhancement Filtering and Global Energy Optimization

Nowadays, a 3-D sub-bottom profiler (SBP) can produce the point clouds of the subseabed and is gradually receiving more attention in getting geologically significant surfaces to reveal sedimentary environments and structural features. However, little literature studied the automatic extraction of these surfaces from the 3-D SBP data currently. Thus, this article proposes a hybrid method consisting of a surface extraction algorithm and a segmentation algorithm. First, the multiprofile SBP data are converted into 3-D data volume. Second, by taking full advantage of the plate-like characteristic of the layer surface in the 3-D SBP data, a plate-like enhancement filtering algorithm based on the nonuniform Gaussian scale is given to filter the 3-D data volume. Third, a threshold extraction is applied to extract surface voxels, and a hybrid region growing algorithm is put forward to segment surface voxels into basic units by combining multicriteria. Finally, the surface segmentation problem is formulated as global energy optimization, and a stepwise segmentation algorithm is proposed to get the final surface set. To verify the effectiveness of the proposed method, experiments were conducted and analyzed. The results showed that the proposed method performed well.