Classifying urban landscape in aerial LiDAR using 3D shape analysis

Authors:
Matthew Carlberg;Peiran Gao;George Chen;Avideh Zakhor
Affiliations:
Video and Image Processing Lab, EECS Department, University of California, Berkeley;Video and Image Processing Lab, EECS Department, University of California, Berkeley;Video and Image Processing Lab, EECS Department, University of California, Berkeley;Video and Image Processing Lab, EECS Department, University of California, Berkeley
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 4
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Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation

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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 multicategory 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 7km2. We show that our ground, roof, and tree classifiers, when trained on one dataset, perform well on the other dataset.