Automatic extraction technique of residential areas in high resolution remote sensing image

Jiannong Cao, Pinglu Wang, Yuwei Dong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


This paper presents an unsupervised segmentation method based on the feature decomposition used for extracting residential areas in high resolution remote sensing image. The method was realized by multi-scale feature analysis with wavelet decomposition, constituting the feature space from differences of the internal, external structure in residential areas and the average spectral radiant intensity, making a self-adaptive segmentation by the constraint mean shift algorithm to texture features, and achieve the automatic extraction of residential areas. Experiment results show that the proposed method can eliminate the influence from over-detailed images and other factors caused by high resolution on the extraction of residential areas, and thus extract residential areas more effectively.
Original languageEnglish
Pages (from-to)831-837
Number of pages7
JournalWuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University
Issue number7
Publication statusPublished - 1 Jan 2014
Externally publishedYes


  • Constraint mean shift
  • Image segmentation
  • Unsupervised segmentation
  • Wavelet decomposion

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Earth-Surface Processes

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