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Imaging spectroscopy records the solar reflected spectrum at a fine spectral resolution and in a large number of bands thereby producing a spectral profile associated with each pixel in an image. This type of data tends to be highly correlated and we intend to harness the information of this spectral dependence by introducing the S-space concept. This concept in conjunction with measures of spatial dependence allows one to visualize the spectral profile as a regionalized variable where distance is measured in wavelengths. Unlike image space, S-space is one-dimensional. We illustrate the S-space concept using a CASI image of a forest scene and an AVIRIS image of an urban scene. This new technique provides spectral correlation information for each individual spectral profile on a per-pixel basis rather than the spectral variability across the entire image as is traditionally done in remote sensing investigations. As an example of the possibilities, spectral dependence was quantified using the semivariogram in S-space. A model of spatial dependence was then fitted to each semivariogram and the model parameters used as input to a classification algorithm in order to extract land cover information. To compare our approach with standard techniques, we used the first three principal components to produce a land cover classification. The semivariogram model parameter derived classification results displayed a better spatial contiguity and greatly diminished the dimensionality of the dataset. We also discuss future directions for the use of the S-space concept.