An explicit index for assessing the accuracy of cover-class areas

Guofan Shao, Wenchun We, Gang Wu, Xinhua Zhou, Jianguo Wu

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


We present a new index, called Relative Errors of Area (REA), for assessing the accuracy of cover-class areal percentage (%LAND) that is extracted from thematic maps after classifying remotely sensed data. We demonstrate how to derive REA from an error matrix and its relationship with user's and producer's accuracy. We compare the REA index with other accuracy indices in a hypothetical and two real case studies. The accuracy of cover-class areal estimates is highly correlated with the REA index, but not with other classification accuracy indices such as the overall classification accuracy. In general, users should beware of using thematic maps with low REA values. Moreover, the estimates of cover-class area can be revised by using REA if cell values of the major diagonal in an error matrix are available.

Original languageEnglish (US)
Pages (from-to)907-913
Number of pages7
JournalPhotogrammetric Engineering and Remote Sensing
Issue number8
StatePublished - Aug 1 2003

ASJC Scopus subject areas

  • Computers in Earth Sciences


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