In remote sensing image processing, relaxation is defined as a method that uses the local relationship among neighboring pixels to correct spectral or spatial distortions. In recent years, relaxation methods have shown great success in classification of remotely sensed data. Relaxation, as a preprocessing step, can reduce noise and improve the class separability in the spectral domain. On the other hand, relaxation (as a postprocessing approach) works on the label image or class probabilities obtained from pixelwise classifiers. In this work, we develop a discontinuity preserving relaxation strategy, which can be used for postprocessing of class probability estimates, as well as preprocessing of the original hyperspectral image. The newly proposed method is an iterative relaxation procedure, which exploits spatial information in such a way that it considers discontinuities existing in the data cube. Our experimental results indicate that the proposed methodology leads to state-of-the-art classification results when combined with probabilistic classifiers for several widely used hyperspectral data sets, even when very limited training samples are available.