Digital Coast and

Davidson, Margaret; Miglarese, Anne Hale

doi:10.14358/pers.69.10.1127

Cited by 10 publications

(1 citation statement)

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“…The volume of bathymetric lidar point clouds available to coastal scientists, engineers, and coastal zone managers is increasing rapidly. Many datasets are available on portals, such as the National Oceanic and Atmospheric Administration (NOAA) Digital Coast [1], state GIS clearinghouses, and others. However, a current impediment to effective use of these bathymetric lidar point clouds is that many lack accurate point classification (specifically, the segmentation of points into water surface, water column noise, and seafloor points) [2,3].…”

Section: Introductionmentioning

confidence: 99%

Inverse Histogram-Based Clustering Approach to Seafloor Segmentation from Bathymetric Lidar Data

2021

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A current hindrance to the scientific use of available bathymetric lidar point clouds is the frequent lack of accurate and thorough segmentation of seafloor points. Furthermore, scientific end-users typically lack access to waveforms, trajectories, and other upstream data, and also do not have the time or expertise to perform extensive manual point cloud editing. To address these needs, this study seeks to develop and test a novel clustering approach to seafloor segmentation that solely uses georeferenced point clouds. The proposed approach does not make any assumptions regarding the statistical distribution of points in the input point cloud. Instead, the approach organizes the point cloud into an inverse histogram and finds a gap that best separates the seafloor using the proposed peak-detection method. The proposed approach is evaluated with datasets acquired in Florida with a Riegl VQ-880-G bathymetric LiDAR system. The parameters are optimized through a sensitivity analysis with a point-wise comparison between the extracted seafloor and ground truth. With optimized parameters, the proposed approach achieved F1-scores of 98.14–98.77%, which outperforms three popular existing methods. Further, we compared seafloor points with Reson 8125 MBES hydrographic survey data. The results indicate that seafloor points were detected successfully with vertical errors of −0.190 ± 0.132 m and −0.185 ± 0.119 m (μ ± σ) for two test datasets.

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Section: Introductionmentioning

confidence: 99%