3D object-based classification for vehicle extraction from airborne LiDAR data by combining point shape information with spatial edge

Wei Yao, Stefan Hinz, Uwe Stilla

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

10 Citations (Scopus)

Abstract

The problem of vehicle extraction using airborne laser scanning (ALS) is studied under the framework of object-based point cloud analysis (OBPA). Object extraction relies on the partitioning of raw ALS data into various segments approximating semantic entities followed by classification. A 3D segmentation method working directly on point cloud is used, which features the detection of local arbitrary modes and the globally optimized organization of segments concurrently. To make the segmentation more competent in extracting small-scale objects such as vehicle, the detection of local structures is realized by adaptive mean shift (MS) using variable bandwidths which are determined by the point shape information bounded by spatial edge. The experimental results show that the proposed method performs very well in terms of visual interpretation as well as extraction accuracy.
Original languageEnglish
Title of host publication2010 IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2010
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event6th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2010 - Istanbul, Turkey
Duration: 22 Aug 201022 Aug 2010

Conference

Conference6th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2010
CountryTurkey
CityIstanbul
Period22/08/1022/08/10

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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