High spatial resolution satellite imagery provides excellent opportunities for detecting fine spatial details on the ground. However, shadows that exist in high resolution images also create problems for land cover classification and environmental application. In various image classification studies, shadows have been either left unclassified or expediently assigned as a class. The class of shadow is not informational and as a result, the real land cover under shadows remains unknown. Furthermore, when using high resolution images to address environmental issues, we most likely need to assess the condition of land cover in addition to identifying land cover types. But radiance values of shadow pixels are contaminated and thus cannot be directly applied to quantitative environmental assessment. A multistage approach for detecting and correcting shadows is proposed in this study. QuickBird imagery of the Falcon Heights - Roseville area in Minnesota was used as an example to examine the results of the proposed methods. We first measured the spectral radiance and reflectance of different types of shadows. These spectral radiometric properties were examined and used as the basis for reclassifying shadows to different information classes and for obtaining shadow-free radiance values. A two-step ISODATA clustering algorithm was employed to detect shadowed areas. The detected shadow areas were corrected by the K-nearest neighbor algorithm and the linear correlation correction method for predicting values of shadow pixels. The classification accuracy and visual appearances were compared to evaluate the effectiveness of spectral radiometric restoration.