CLASSIFICATION of AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES and DIFFERENT NEIGHBOURHOOD TYPES

R. Blomley, B. Jutzi, M. Weinmann

Research output: Contribution to journalConference articlepeer-review

41 Scopus citations

Abstract

In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.

Original languageEnglish
Pages (from-to)169-176
Number of pages8
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume3
DOIs
StatePublished - 2 Jun 2016
Externally publishedYes
Event23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016 - Prague, Czech Republic
Duration: 12 Jul 201619 Jul 2016

Keywords

  • ALS
  • Classification
  • Features
  • LiDAR
  • Multi-Scale
  • Point Cloud

Fingerprint

Dive into the research topics of 'CLASSIFICATION of AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES and DIFFERENT NEIGHBOURHOOD TYPES'. Together they form a unique fingerprint.

Cite this