Jilong Chen

Restoration of shadows in multispectral imagery using surface reflectance relationships with nearby similar areas

The presence of shadows in optical satellite images limits the application of remote-sensing technology. It is important to restore shadow radiance information for improving information extraction from remote-sensing images. Several shadow-restoration methods have been developed using complex statistical relationships between shadowed areas and their nearby sunlit areas. In this study, a simple shadow-restoration approach was proposed based on the surface reflectance equality relationship (RER) under the assumption that the surface reflectance of a feature in the shadowed area is equal to that of the same feature in the nearby sunlit area. This approach reduces the number of parameters, thus reducing the error propagated by the uncertainties of extra parameters. The new RER method was tested with three multispectral images with different shadow features. By comparing RER with the widely used mean and variance transformation, the RER was shown to be capable of restoring the image colours, texture, tone, and brightness of the shadowed areas to a visually satisfactory level. Quantitative analysis suggests that RER can help to restore the reflectance of shadow features accurately and has robust performance for a variety of land-surface types. Moreover, RER can be effectively used to restore the spectral shape information of shadow features, which is particularly important when applying RER to the restoration of multispectral imagery for the purpose of image classification.