Determining uncertainties and their propagation in dynamic change detection based on classified remotely-sensed images

Wen Zhong Shi, M. Ehlers

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)

Abstract

This paper provides an approach to determine uncertainties and their propagation in dynamic change detection based on classified remotely-sensed images. First, the uncertainties of a classified image using maximum likelihood (ML) classification are determined. The probability vectors which are generated during the maximum likelihood classification are used as uncertainty indicators. Secondly, the uncertainty propagation of classified multi-date images is described using mathematical language for problem description. Based on this mathematical formulation, two techniques were used to calculate the uncertainty propagation. One is based on the product rule in probability theory and the other is based on a certainty factor model with probabilistic interpretation. Thirdly, a visualization technique, using 3-D and colour, was developed to present uncertainties.
Original languageEnglish
Pages (from-to)2729-2741
Number of pages13
JournalInternational Journal of Remote Sensing
Volume17
Issue number14
DOIs
Publication statusPublished - 1 Sep 1996

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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