Level set evolution with local uncertainty constraints for unsupervised change detection

Xiaokang Zhang, Wenzhong Shi, Peng Liang, Ming Hao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


This letter presents a novel method for unsupervised change detection (CD) from remote sensing images using level set evolution with local uncertainty constraints (LSELUC). Uncertainty analysis of pixel labels was implemented as prior information to guide the evolution of level curves. Then, local uncertainty and gradient information of level curves were incorporated into the level set energy function to construct local energy constraints. The proposed method can reduce noise, while preserving details in change regions. Furthermore, an advanced regularization strategy of the level set function was adopted to improve the computational efficiency. The performance of the proposed method was validated on two remote sensing data sets. Experimental results show that the proposed method can produce satisfactory CD results.

Original languageEnglish
Pages (from-to)811-820
Number of pages10
JournalRemote Sensing Letters
Issue number8
Publication statusPublished - 3 Aug 2017

ASJC Scopus subject areas

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


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