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Comparison of an L1-regression-based and a RANSAC-based planar segmentation procedure for urban terrain data with many outliers

 
: Luo, Jian; Zeng, Zhibin; Bulatov, Dimitri; Lavery, John E.; Shu-Chern Fang

:
Postprint urn:nbn:de:0011-n-2671668 (656 KByte PDF)
MD5 Fingerprint: efb3424f694fda2fd44cf1a04c161bec
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 28.11.2013


Bruzzone, L. (Ed.) ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Image and Signal Processing for Remote Sensing XIX : Dresden, Germany, September 23, 2013
Bellingham, WA: SPIE, 2013 (Proceedings of SPIE 8892)
ISBN: 978-0-8194-9761-1
Paper 889209
Conference "Image and Signal Processing for Remote Sensing" <19, 2013, Dresden>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
DBSCAN; fuzzy c-means; outlier-rich; planar; RANSAC; segmentation; urban terrain

Abstract
For urban terrain data with many outliers, we compare an ℓ1-regression-based and a RANSAC-based planar segmentation procedure. The procedure consists of 1) calculating the normal at each of the points using ℓ1 regression or RANSAC, 2) clustering the normals thus generated using DBSCAN or fuzzy c-means, 3) within each cluster, identifying segments (roofs, walls, ground) by DBSCAN-based-subclustering of the 3D points that correspond to each cluster of normals and 4) fitting the subclusters by the same method as that used in Step 1 (ℓ1 regression or RANSAC). Domain decomposition is used to handle data sets that are too large for processing as a whole. Computational results for a point cloud of a building complex in Bonnland, Germany obtained from a depth map of seven UAV-images are presented. The ℓ1-regression-based procedure is slightly over 25% faster than the RANSAC-based procedure and produces better dominant roof segments. However, the roof polygonalizations and cutlines based on these dominant segments are roughly equal in accuracy for the two procedures. For a set of artificial data, ℓ1 regression is much more accurate and much faster than RANSAC. We outline the complete building reconstruction procedure into which the ℓ1-regression-based and RANSAC-based segmentation procedures will be integrated in the future.

: http://publica.fraunhofer.de/dokumente/N-267166.html