A new image classification technique for analysis of remotely-sensed data based on geostatistical indicator kriging is introduced. Conventional classification techniques require ground truth information, use only the spectral characteristics of an unknown pixel in comparison, rely on a Gaussian probability distribution for the spectral signature of the training data, and work on a pixel support or spatial resolution without allowing classification on larger or smaller volumes. The indicator kriging classifier overcomes such problems because: (1) it relies on spectral information from laboratory studies rather than on ground truth data, (2) through the kriging estimation variances an estimate of uncertainly is derived, (3) it incorporates spatial aspects because it uses local estimation techniques, (4) it is distribution-free, (5) and may be applied on different supports if the data are corrected for support changes. Comparison of classification results applied to the problem of mapping calcite and dolomite from GER imaging spectrometry data shows that indicator kriging performs better than the conventional classification algorithms and gives insight in the accuracy of the results without prior field knowledge