NDLI: Classifying urban landscape in aerial LiDAR using 3D shape analysis

Content Provider	IEEE Xplore Digital Library
Author	Carlberg, M. Gao, P. Chen, G. Zakhor, A.
Copyright Year	2009
Description	Author affiliation: Video and Image Processing Lab, EECS Department, University of California, Berkeley, USA (Carlberg, M.; Gao, P.; Chen, G.; Zakhor, A.)
Abstract	The classification of urban landscape in aerial LiDAR point clouds is useful in 3D modeling and object recognition applications in urban environments. In this paper, we introduce a multi-category classification system for identifying water, ground, roof, and trees in airborne LiDAR. The system is organized as a cascade of binary classifiers, each of which performs unsupervised region growing followed by supervised, segment-wise classification. Categories with the most discriminating features, such as water and ground, are identified first and are used as context for identifying more complex categories, such as trees. We use 3D shape analysis and region growing to identify “planar” and “scatter” regions that likely correspond to ground/roof and trees respectively. We demonstrate results on two urban datasets, the larger of which contains 200 million LiDAR returns over $7km^{2}.$ We show that our ground, roof, and tree classifiers, when trained on one dataset, perform well on the other dataset.
Starting Page	1701
Ending Page	1704
File Size	1023477
Page Count	4
File Format	PDF
ISBN	9781424456536
ISSN	15224880
e-ISBN	9781424456543
e-ISBN	9781424456550
DOI	10.1109/ICIP.2009.5413385
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laser radar Shape Clouds Classification tree analysis Light scattering Object recognition Image segmentation Cities and towns Image analysis Image processing 3D shape analysis Airborne LiDAR cascaded classifiers region growing
Content Type	Text
Resource Type	Article

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