  The motivation of this paper is to conduct a comparative study on remote
sensing image classification using the morphological attribute profiles (APs)
and feature profiles (FPs) generated from different types of tree structures.
Over the past few years, APs have been among the most effective methods to
model the image's spatial and contextual information. Recently, a novel
extension of APs called FPs has been proposed by replacing pixel gray-levels
with some statistical and geometrical features when forming the output
profiles. FPs have been proved to be more efficient than the standard APs when
generated from component trees (max-tree and min-tree). In this work, we
investigate their performance on the inclusion tree (tree of shapes) and
partition trees (alpha tree and omega tree). Experimental results from both
panchromatic and hyperspectral images again confirm the efficiency of FPs
compared to APs.
