TY - JOUR
T1 - Robust rooftop extraction from visible band images using higher order CRF
AU - Li, Er
AU - Femiani, John
AU - Xu, Shibiao
AU - Zhang, Xiaopeng
AU - Wonka, Peter
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the National Natural Science Foundation of China under Grant 61331018, Grant 91338202, and Grant 61100132.
PY - 2015/8
Y1 - 2015/8
N2 - In 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.
AB - In 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.
UR - http://hdl.handle.net/10754/564195
UR - http://ieeexplore.ieee.org/document/7047875/
UR - http://www.scopus.com/inward/record.url?scp=84926222218&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2015.2400462
DO - 10.1109/TGRS.2015.2400462
M3 - Article
SN - 0196-2892
VL - 53
SP - 4483
EP - 4495
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 8
ER -