Application of LiDAR's multiple attributes for wetland classification

Qiong Ding, Shengyue Ji, Wu Chen

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

Abstract

Wetlands have received intensive interdisciplinary attention as a unique ecosystem and valuable resources. As a new technology, the airborne LiDAR system has been applied in wetland research these years. However, most of the studies used only one or two LiDAR observations to extract either terrain or vegetation in wetlands. This research aims at integrating LiDAR's multiple attributes (DSM, DTM, off-ground features, Slop map, multiple pulse returns, and normalized intensity) to improve mapping and classification of wetlands based on a multi-level object-oriented classification method. By using this method, we are able to classify the Yellow River Delta wetland into eight classes with overall classification accuracy of 92.5%
Original languageEnglish
Title of host publication2nd ISPRS International Conference on Computer Vision in Remote Sensing, CVRS 2015
PublisherSPIE
Volume9901
ISBN (Electronic)9781510601543
DOIs
Publication statusPublished - 1 Jan 2016
Event2nd ISPRS International Conference on Computer Vision in Remote Sensing, CVRS 2015 - Xiamen, China
Duration: 28 Apr 201530 Apr 2015

Conference

Conference2nd ISPRS International Conference on Computer Vision in Remote Sensing, CVRS 2015
Country/TerritoryChina
CityXiamen
Period28/04/1530/04/15

Keywords

  • accuracy
  • classification
  • LiDAR
  • multiple attributes
  • wetlands

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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