Multivariate statistical analysis of measures for assessing the quality of image fusion

Shuang Li, Zhilin Li, Jianya Gong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

61 Citations (Scopus)

Abstract

Various measures are available for assessing image fusion quality. Some measures are from traditional image quality assessment and some are specially designed for image fusion evaluation. It has been found from a survey that there is a total of 27 measures in common use. It can be imagined that some of them are more reliable than others for certain applications and some of them may be quite highly correlated. Therefore, a thorough mathematical analysis of these measures is desirable to understand what measures should be adopted for a given application. This article describes a multivariate statistical analysis of these measures to reduce redundancy and find comparatively independent measures for assessing the quality of fused images. First, correlation coefficients are calculated for the 27 measures; then, factor analysis using principal components is performed based on correlation matrix; and finally, hierarchical clustering is carried out on the factors to obtain finer clusters and to find representative measures. Experiments are carried out on 144 fused images. Based on the results, the 27 measures are classified into five categories: Difference-based, noise-based, similarity-based, information-clarity-based and overall-based. Further, the most representative measure is selected from each category as a recommendation.
Original languageEnglish
Pages (from-to)47-66
Number of pages20
JournalInternational Journal of Image and Data Fusion
Volume1
Issue number1
DOIs
Publication statusPublished - 1 Jan 2010

Keywords

  • Factor analysis
  • Hierarchical cluster analysis
  • Image fusion
  • Multivariate statistical analysis
  • Principal component
  • Quantitative quality assessment

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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