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Accuracy assessment of maps relies on the collection of validation data, i.e., a set of trusted points or spatial objects collected independently from the classified map. However, collecting spatially and thematically accurate dataset is often tedious and expensive. Despite good practices, those datasets are rarely error-prone. Errors in the reference dataset propagate to the probabilities estimated in the confusion matrices. Consequently, the estimates of the quality are biased: accuracy indices are overestimated if the errors are correlated and underestimated if the errors are conditionally independent. The first findings of our study highlight the fact that this bias could invalidate statistical tests of map accuracy assessment. Furthermore, correlated errors in the reference dataset induce unfair comparison of classifiers. A maximum entropy method is thus proposed to mitigate the propagation of errors from imperfect reference datasets. The proposed method is based on a theoretical framework which considers a trivariate probability table that links the observed confusion matrix, the confusion matrix of the reference dataset and the “real” confusion matrix. The method was tested with simulated thematic and geo-reference errors. It proved to reduce the bias to the level of the sampling uncertainty. The method was very efficient with geolocation errors because conditional independence of errors can reasonably be assumed. Thematic errors are more difficult to mitigate because they require the estimation of an additional parameter related to the amount of spatial correlation. In any case, while collecting additional trusted labels is usually expensive, our result show that the benefits for accuracy assessment are much larger than collecting a larger number of questionable reference data.

About the Pitfall of Erroneous Validation Data in the Estimation of Confusion Matrices