Robust rooftop extraction from visible band images using higher order CRF
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Visual Computing Center (VCC)
Permanent link to this recordhttp://hdl.handle.net/10754/564195
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AbstractIn this paper, we propose a robust framework for building extraction in visible band images. We first get an initial classification of the pixels based on an unsupervised presegmentation. Then, we develop a novel conditional random field (CRF) formulation to achieve accurate rooftops extraction, which incorporates pixel-level information and segment-level information for the identification of rooftops. Comparing with the commonly used CRF model, a higher order potential defined on segment is added in our model, by exploiting region consistency and shape feature at segment level. Our experiments show that the proposed higher order CRF model outperforms the state-of-the-art methods both at pixel and object levels on rooftops with complex structures and sizes in challenging environments. © 1980-2012 IEEE.
CitationLi, E., Femiani, J., Xu, S., Zhang, X., & Wonka, P. (2015). Robust Rooftop Extraction From Visible Band Images Using Higher Order CRF. IEEE Transactions on Geoscience and Remote Sensing, 53(8), 4483–4495. doi:10.1109/tgrs.2015.2400462
SponsorsThis work was supported by the National Natural Science Foundation of China under Grant 61331018, Grant 91338202, and Grant 61100132.