Remote sensing images provide a valuable source of information about the earth’s surface. The presence
of shadow can reduce the amount of information that can be extracted from these images.
Shadow in remote sensing images is produced due to blockage of a direct light by an object. In spite
of the reflectance gathered in the shadow area being weak, there is still valuable information that
makes shadow restoration possible. Shadow restoration process consists of 2 main steps: detection
and compensation. Various algorithms and methods have been developed to perform these 2 steps.
These algorithms differ according to the objects causing shadow and types of sensor. Consequently,
it is important to review the different approaches that have been employed in shadow correction
research to delineate their suitability for a specific application. This article is aimed at reviewing various
shadow detection and compensation techniques with their methods of evaluation, taking into
consideration objects causing shadow and type of sensor used. Also, it gives discussion and recommendations
to enhance the performance of existing methods.