Large portions of shadowed areas in satellite images of urban areas can affect the accuracy of classification and thus reduce an image's effectiveness in urban remote sensing applications. This is particularly acute in cities such as Hong Kong where dense high-rise buildings cast many long shadows across a variety of different surface types. One solution to this problem is to enhance shadowed areas so their spectral range becomes closer to their corresponding non-shadowed areas. Shadowed areas were automatically selected and two techniques, Gamma correction and Linear Correlation Correction, were applied to three study sites of a 2.4 m Quickbird image. The selected study sites represent typical urban types of Hong Kong, ranging from high-rise commercial to low-rise residential areas. The shadow detection algorithm is based on the spectral shape index and its limitation is discussed. The histograms of the corresponding non-shadowed areas, the original and the enhanced shadow areas are used to compare the spectral range. The results show that the enhanced areas, in band ratios such as NDVI, show greater similarity after enhancement, but they also look darker than the non-shadowed areas. Where continuous shadowed areas such as in commercial areas, the spectral range cannot be restored.