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Proceedings Paper

Learning-based roof style classification in 2D satellite images
Author(s): Andi Zang; Xi Zhang; Xin Chen; Gady Agam
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Paper Abstract

Accurately recognizing building roof style leads to a much more realistic 3D building modeling and rendering. In this paper, we propose a novel system for image based roof style classification using machine learning technique. Our system is capable of accurately recognizing four individual roof styles and a complex roof which is composed of multiple parts. We make several novel contributions in this paper. First, we propose an algorithm that segments a complex roof to parts which enable our system to recognize the entire roof based on recognition of each part. Second, to better characterize a roof image, we design a new feature extracted from a roof edge image. We demonstrate that this feature has much better performance compared to recognition results generated by Histogram of Oriented Gradient (HOG), Scale-invariant Feature Transform (SIFT) and Local Binary Patterns (LBP). Finally, to generate a classifier, we propose a learning scheme that trains the classifier using both synthetic and real roof images. Experiment results show that our classifier performs well on several test collections.

Paper Details

Date Published: 21 May 2015
PDF: 11 pages
Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 94730K (21 May 2015); doi: 10.1117/12.2180393
Show Author Affiliations
Andi Zang, Illinois Institute of Technology (United States)
Xi Zhang, Illinois Institute of Technology (United States)
Xin Chen, HERE., a Nokia Company (United States)
Gady Agam, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9473:
Geospatial Informatics, Fusion, and Motion Video Analytics V
Matthew F. Pellechia; Kannappan Palaniappan; Peter J. Doucette; Shiloh L. Dockstader; Gunasekaran Seetharaman; Paul B. Deignan, Editor(s)

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