Shoreline extraction from the integration of LiDAR point cloud data and aerial orthophotos using mean shift segmentation

I. Chieh Lee, Bo Wu, Ron Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

23 Citations (Scopus)

Abstract

A method for shoreline extraction from integrated LiDAR point cloud data and aerial orthophotos is presented. First, a Mean Shift Algorithm is used for LiDAR point segmentation. The horizontal position and elevation of the LiDAR point plus color information obtained from the corresponding orthophoto are used as the point features in the Mean Shift Algorithm. Due to the homogenous nature of the elevation and color distribution of a water surface, LiDAR points distributed on the water surface and on the ground can be classified using Mean Shift Algorithm in a semi-supervised manner. Second, a modified convex hull algorithm is used to determine the boundary of the classified LiDAR points. The shoreline is defined as the result of the separation boundary between the LiDAR points belonging to water and those belonging to non-water. The experiment, which used LiDAR data and orthophotos acquired at the same time in Portsmouth, New Hampshire, shows that the accuracy of the derived shoreline is an improvement over LiDAR point spacing.
Original languageEnglish
Title of host publicationAmerican Society for Photogrammetry and Remote Sensing Annual Conference 2009, ASPRS 2009
Pages489-495
Number of pages7
Volume2
Publication statusPublished - 1 Dec 2009
Externally publishedYes
EventAmerican Society for Photogrammetry and Remote Sensing Annual Conference 2009, ASPRS 2009 - Baltimore, MD, United States
Duration: 9 Mar 200913 Mar 2009

Conference

ConferenceAmerican Society for Photogrammetry and Remote Sensing Annual Conference 2009, ASPRS 2009
Country/TerritoryUnited States
CityBaltimore, MD
Period9/03/0913/03/09

ASJC Scopus subject areas

  • Information Systems
  • Computers in Earth Sciences

Cite this