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Andi Zang

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Proceedings Article | 21 May 2015 Paper

Learning-based roof style classification in 2D satellite images

Andi Zang, Xi Zhang, Xin Chen, Gady Agam

Proceedings Volume 9473, 94730K (2015) https://doi.org/10.1117/12.2180393

KEYWORDS: Image segmentation, Satellite imaging, Earth observing sensors, Satellites, Detection and tracking algorithms, Image classification, Image processing algorithms and systems, Edge detection, 3D modeling, Data modeling

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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.

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