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GeoInformatica

, Volume 11, Issue 1, pp 37–53 | Cite as

LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification

  • Xiaoye LiuEmail author
  • Zhenyu Zhang
  • Jim Peterson
  • Shobhit Chandra
Article

Abstract

Orthophotos (or orthoimages if in digital form) have long been recognised as a supplement or alternative to standard maps. The increasing applications of orthoimages require efforts to ensure the accuracy of produced orthoimages. As digital photogrammetry technology has reached a stage of relative maturity and stability, the availability of high quality ground control points (GCPs) and digital elevation models (DEMs) becomes the central issue for successfully implementing an image orthorectification project. Concerns with the impacts of the quality of GCPs and DEMs on the quality of orthoimages inspire researchers to look for more reliable approaches to acquire high quality GCPs and DEMs for orthorectification. Light Detection and Ranging (LiDAR), an emerging technology, offers capability of capturing high density three dimensional points and generating high accuracy DEMs in a fast and cost-effective way. Nowadays, highly developed computer technologies enable rapid processing of huge volumes of LiDAR data. This leads to a great potential to use LiDAR data to get high quality GCPs and DEMs to improve the accuracy of orthoimages. This paper presents methods for utilizing LiDAR intensity images to collect high accuracy ground coordinates of GCPs and for utilizing LiDAR data to generate a high quality DEM for digital photogrammetry and orthorectification processes. A comparative analysis is also presented to assess the performance of proposed methods. The results demonstrated the feasibility of using LiDAR intensity image-based GCPs and the LiDAR-derived DEM to produce high quality orthoimages.

Keywords

orthorectification orthoimage LiDAR LiDAR intensity DEM 

Notes

Acknowledgment

The authors would like to thank three anonymous reviewers for their valuable comments and suggestions. We are also grateful to the Corangamite Catchment Management Authority for providing LiDAR data and other datasets to support this project.

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Xiaoye Liu
    • 1
    Email author
  • Zhenyu Zhang
    • 1
  • Jim Peterson
    • 1
  • Shobhit Chandra
    • 1
  1. 1.Center for GIS, School of Geography and Environmental ScienceMonash UniversityMelbourneAustralia

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