Spatial-attraction-based Markov random field approach for classification of high spatial resolution multispectral imagery

Hua Zhang, Wen Zhong Shi, Yunjia Wang, Ming Hao, Zelang Miao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


This letter presents a novel spatial-attraction-based Markov random field (MRF) (SAMRF) approach for high spatial resolution multispectral imagery (HSRMI) classification. First, the initial class label and class membership for each pixel are obtained by applying the maximum likelihood classifier (MLC) classification for the HSRMI. Second, to reduce the oversmooth classification in the traditional MRF, an adaptive weight MRF model is introduced by integrating the spatial attraction model into the traditional MRF. Finally, the initial classification map, generated in the first step, will be refined though the SAMRF regularization. Two different experiments were performed to evaluate the performance of the SAMRF, in comparison with standard MLC and MRF. Experimental results indicate that the SAMRF method achieved the highest accuracy, hence, providing an effective spectral-spatial classification method for the HSRMI.
Original languageEnglish
Pages (from-to)489-493
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number2
Publication statusPublished - 1 Jan 2014


  • Classification
  • High spatial resolution multispectral imagery (HSRMI)
  • Markov random field (MRF)
  • Spatial attraction

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

Cite this