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Multispectral remote sensing images are widely used for landuse/landcover (LULC) classification. Performance of such classificationpractices is normally evaluated through the confusion matrix which summarizes the producer’s and user’s accuracies and the overallaccuracy. However, the confusion matrix is based on the classification results of a set of multi-class training data. As a result, theclassification accuracies are heavily dependent on the representativeness of the training data set and it is imperative for practitionersto assess the uncertainties of LULC classification in order for a full understanding of the classification results. In addition, theGaussian-based maximum likelihood classifier (GMLC) is widely applied in many practices of LULC classification. The GMLCassumes the classification features jointly form a multivariate normal distribution, whereas as, in reality, many features of individuallandcover classes have been found to be non-Gaussian. Direct application of GMLC will certainly affect the classification results. Ina pilot study conducted in Taipei and its vicinity, we tackled these two problems by firstly transforming the original training data setto a corresponding data set which forms a multivariate normal distribution before conducting LULC classification using GMLC. Wethen applied the bootstrap resampling technique to generate a large set of multi-class resampled training data from the multivariatenormal training data set. LULC classification was then implemented for each resampled training data set using the GMLC. Finally,the uncertainties of LULC classification accuracies were assessed by evaluating the means and standard deviations of the producer’sand user’s accuracies of individual LULC classes which were derived from a set of confusion matrices. Results of this studydemonstrate that Gaussian-transformation of the original training data achieved better classification accuracies and the bootstrapresampling technique is a very helpful tool for assessing uncertainties of LULC classification. The uncertainties in classificationaccuracies were also found to be affected by the sizes of class-specific training samples

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

ASSESSING UNCERTAINTIES IN ACCURACY OF LANDUSE CLASSIFICATION USING REMOTE SENSING IMAGES