High spatial resolution images available by satellites such as
Ikonos, Quickbird, and WorldView-2 provide more information for
remote sensing applications, such as object detection, classification,
change detection, and object mapping. The presence of
shadow reduces the amount of information that can be extracted
and consequently makes these applications more difficult or even
impossible. In this article, a shadow restoration approach for highresolution
satellite images is proposed. The approach detects the
shadow area and segments the image into regions according to
the land surface type. Then, shadow restoration is carried out for
each region based on the degree of correspondence between
shadow and neighbouring non-shadow regions. The proposed
approach is applied to study areas from Ikonos and WorldView-2
satellite images. A comparison to the standard approaches for
shadow restoration is performed, and an accuracy assessment is
carried out by visual inspection and land-cover classification. The
results show that the enhanced shadow regions using the proposed
approach have better appearances and are highly compatible
with their surrounding non-shadow regions. In addition, the
overall accuracy is higher than those of the standard approaches.