Spectral similarity measures can differentiate subtle differences between spectral footprints extracted from remotely sensed images. Prior research on urban scenes examined low-to-medium resolution images where many details of an urban scene are lost. For high spatial resolution imagers, a number of problems arise in the analysis of urban scenes since virtually all objects are resolved. Issues of spatially varying scene illumination and unavailability of surface reflectance data prevent classical spectral similarity measures from being extensively applied. Commonly used measures have limitations from a feature space perspective such as favoring either spectral direction or spectral magnitude. In this paper, a novel spectral similarity measure based on Mahalanobis distance is proposed to take into account the unique properties of high resolution urban scenes. A simplified radiometric transfer model is also incorporated. The results confirm the advantage of the new spectral similarity measure when applied to complex urban images.