  Morphological attribute profiles (APs) are among the most effective methods
to model the spatial and contextual information for the analysis of remote
sensing images, especially for classification task. Since their first
introduction to this field in early 2010's, many research studies have been
contributed not only to exploit and adapt their use to different applications,
but also to extend and improve their performance for better dealing with more
complex data. In this paper, we revisit and discuss different developments and
extensions from APs which have drawn significant attention from researchers in
the past few years. These studies are analyzed and gathered based on the
concept of multi-stage AP construction. In our experiments, a comparative study
on classification results of two remote sensing data is provided in order to
show their significant improvements compared to the originally proposed APs.
