Learning-based roof style classification in 2D satellite images

Andi Zang; Xi Zhang; Xin Chen; Gady Agam

doi:10.1117/12.2180393

21 May 2015 Learning-based roof style classification in 2D satellite images

Andi Zang, Xi Zhang, Xin Chen, Gady Agam

Proceedings Volume 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V; 94730K (2015) https://doi.org/10.1117/12.2180393
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Andi Zang, Xi Zhang, Xin Chen, and Gady Agam "Learning-based roof style classification in 2D satellite images", Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 94730K (21 May 2015); https://doi.org/10.1117/12.2180393

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