Empirical radiometric normalization of road points from terrestrial mobile lidar system

Tee-Ann Teo*, Hui Lin Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Lidar data provide both geometric and radiometric information. Radiometric information is influenced by sensor and target factors and should be calibrated to obtain consistent energy responses. The radiometric correction of airborne lidar system (ALS) converts the amplitude into a backscatter cross-section with physical meaning value by applying a model-driven approach. The radiometric correction of terrestrial mobile lidar system (MLS) is a challenging task because it does not completely follow the inverse square range function at near-range. This study proposed a radiometric normalization workflow for MLS using a data-driven approach. The scope of this study is to normalize amplitude of road points for road surface classification, assuming that road points from different scanners or strips should have similar responses in overlapped areas. The normalization parameters for range effect were obtained from crossroads. The experiment showed that the amplitude difference between scanners and strips decreased after radiometric normalization and improved the accuracy of road surface classification.

Original languageEnglish
Pages (from-to)6336-6357
Number of pages22
JournalRemote Sensing
Volume7
Issue number5
DOIs
StatePublished - 1 Jan 2015

Keywords

  • Amplitude
  • Data driven
  • MLS
  • Radiometric normalization
  • Road surface classification

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