Abstract
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.
Original language | English (US) |
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Pages (from-to) | 4483-4495 |
Number of pages | 13 |
Journal | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 53 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2015 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: This work was supported by the National Natural Science Foundation of China under Grant 61331018, Grant 91338202, and Grant 61100132.
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- General Earth and Planetary Sciences