Abstract

Confidence in the conclusions of GIS and remote sensing analyses depends on our ability to specify their accuracy. The square error matrix, which is commonly used for accuracy assessment when the database contains continuous digital choropleth data, allows computation of user's, producer's, overall, and Kappa coefficient accuracy values. However, when the underlying assumptions of the square error matrix method are violated, these accuracy values may become unreliable. We demonstrate how traditional forms of accuracy assessment can fail for discontinuous binary surfaces and provide an improved spatial assessment method. We developed 15 new accuracy measures that allow analysts to assess scene- and feature-level accuracy from many new perspectives.

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