3D Landslide Monitoring in High Spatial Resolution by Feature Tracking and Histogram Analyses Using Laser Scanners

Kourosh Hosseini, Leonhard Reindl, Lukas Raffl, Wolfgang Wiedemann, Christoph Holst

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to 25 percent of all displacement vectors, exemplifying its effectiveness. Key to our methodology is a refined feature tracking process utilizing terrestrial laser scanners, renowned for their precision and detail in capturing surface information. This enhanced feature tracking method allows for more accurate and reliable landslide monitoring, representing a significant advancement in geospatial analysis techniques.

Original languageEnglish
Article number138
JournalRemote Sensing
Volume16
Issue number1
DOIs
StatePublished - Jan 2024

Keywords

  • deformation analysis
  • feature extraction
  • hillshade image
  • laser scanning
  • point cloud

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