Level set incorporated with an improved MRF model for unsupervised change detection for satellite images

Xiaokang Zhang, Wenzhong Shi, Ming Hao, Pan Shao, Xuzhe Lyu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

This study proposes the use of a level set incorporated with an improved Markov random field (MRF) model in unsupervised change detection for satellite images. MRF provides a means of modelling the spatial contextual information in the level set, and an edge indicator function is introduced into the MRF model to control the contribution of local information in the boundary areas to change detection. On the basis of the improved MRF model, local label relationships and edge information are considered in the level set energy functional to conduct a novel local term and attract the contours into desired objects. By merging the novel energy term, the proposed approach not only reduces noise but also obtains accurate outlines of the changed regions. Experimental results obtained with Landsat 7 Enhanced Thematic Mapper Plus and SPOT 5 data sets confirm the superiority of the proposed model when compared with state-of-the-art change detection methods.

Original languageEnglish
Pages (from-to)202-210
Number of pages9
JournalEuropean Journal of Remote Sensing
Volume50
Issue number1
DOIs
Publication statusPublished - 1 Jan 2017

Keywords

  • change detection
  • Level set
  • Markov random field
  • satellite images

ASJC Scopus subject areas

  • Environmental Science(all)
  • Computers in Earth Sciences
  • Atmospheric Science
  • Applied Mathematics

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