Robust rooftop extraction from visible band images using higher order CRF

Er Li, John Femiani, Shibiao Xu, Xiaopeng Zhang, Peter Wonka

Research output: Contribution to journalArticle

50 Scopus citations

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.

Original languageEnglish (US)
Article number7047875
Pages (from-to)4483-4495
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Issue number8
DOIs
StatePublished - Aug 1 2015

Keywords

  • Buildings
  • rooftops conditional random field (CRF)
  • shadows

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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