Object-based classification of terrestrial laser scanning point clouds for landslide monitoring

Andreas Mayr, Martin Rutzinger, Magnus Bremer, Sander Oude Elberink, Felix Stumpf, Clemens Geitner
<span title="">2017</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cj7pes4qvbhm7bkgt2hgw6mhkm" style="color: black;">Photogrammetric Record</a> </i> &nbsp;
Terrestrial laser scanning (TLS) is often used to monitor landslides and other gravitational mass movements with high levels of geometric detail and accuracy. However, unstructured TLS point clouds lack semantic information, which is required to geomorphologically interpret the measured changes. Extracting meaningful objects in a complex and dynamic environment is challenging due to the objects' fuzziness in reality, as well as the variability and ambiguity of their patterns in a morphometric
more &raquo; ... ature space. This work presents a point-cloud-based approach for classifying multitemporal scenes of a hillslope affected by shallow landslides. The 3D point clouds are segmented into morphologically homogeneous and spatially connected parts. These segments are classified into seven target classes (scarp, eroded area, deposit, rock outcrop and different classes of vegetation) in a two-step procedure: a supervised classification step with a machine-learning classifier using morphometric features, followed by a correction step based on topological rules. This improves the final object extraction considerably.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/phor.12215">doi:10.1111/phor.12215</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2bzdtn3nt5grriiqtzacjqp4ha">fatcat:2bzdtn3nt5grriiqtzacjqp4ha</a> </span>
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