Mining remote sensing image data: An integration of fuzzy set theory and image understanding techniques for environmental change detection

Peter Eklund, Jia You, Peter Deer

Research output: Journal article publicationConference articleAcademic researchpeer-review

8 Citations (Scopus)


This paper presents an image understanding approach to mine remotely sensed image data from different source dates for environmental change detection. It is focused on the immediate needs for knowledge discovery from large sets of image data for environmental monitoring. In contrast to the traditional approaches for change detection, we introduce a wavelet-based hierarchical scheme which integrates fuzzy set theory and image understanding techniques for knowledge discovery of the remote image data. The proposed approach includes algorithms for hierarchical change detection, region representations and classification. The effectiveness of the proposed algorithms is demonstrated throughout the completion of three tasks, namely hierarchical detection of change by fuzzy post classification comparison, localization of change by B-spline based region representation, and categorization of change by hierarchical texture classification.
Original languageEnglish
Pages (from-to)265-272
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 3 Dec 2000
Externally publishedYes
EventData Mining and Knowledge Discovery: Theory, Tools, and Technology II - Orlando, FL, United States
Duration: 24 Apr 200025 Apr 2000

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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