We introduce a new feature extraction model for purposes of image comparison, visualization and interpretation. We define the notion of spectral saliency, as the extent to which a certain group of pixels stands out in an image and in terms of reflectance, rather than in terms of colorimetric attributes as it is the case in traditional saliency studies. The model takes as an input a multi- or hyper-spectral image with any dimensionality, any range of wavelengths, and it uses a series of dedicated feature extractions to output a single saliency map. We also present a local analysis of the image spectrum allowing to produce such maps in color, thus depicting not only which objects are salients, but also in which range of wavelengths. A variety of applications can be derived from the resulting maps, particularly under the scope of visualization, such as the saliency-driven evaluation of dimensionality reduction techniques. Results show that spectral saliency provides valuable information, which do not correlate neither with visual saliency, second-order statistics nor with naturalness, but serve however well for visualization-related applications.