A fast level set algorithm for building roof recognition from high spatial resolution panchromatic images

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)


Traditional level set methods usually require repeated tuning of parameters, which is quite laborious and thus limits their applications. In order to simplify the parameter setting, this letter presents a fast level set algorithm that is a further extension of the original Chan-Vese model. For computational efficiency, we start by initializing the level set function in our algorithm as a binary step function rather than the often used signed distance function. Then, we eliminate the curvature-based regularizing term that is commonly used in traditional models. Thus, we can use a relatively larger time step in the numerical scheme to expedite our model. Furthermore, to keep the evolving level curves smooth, we introduce a Gaussian kernel into our algorithm to convolve the updated level set function directly. Finally, compared with other existing popular algorithms in an experiment of recognizing building roofs from high spatial resolution panchromatic images, the proposed model is much more computationally efficient while object recognition performance is comparable to other popular models.
Original languageEnglish
Article number6590020
Pages (from-to)743-747
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number4
Publication statusPublished - 1 Apr 2014


  • Building roof recognition
  • Chan-Vese (CV) model
  • fast level set algorithm
  • high spatial resolution
  • panchromatic image

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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