Morphological attribute profiles (APs) are among the most effective spatial-spectral methods to perform multilevel image description based on hierarchical tree-based representation. They have been widely applied to the processing and characterization of remote sensing images, in particular to tackle classification task, in the literature. 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 both inclusion and partition trees. The motivation of this article is to conduct a comparative study of APs and FPs using different attributes including some novel ones that have not been used in the literature. We also present our developed library called Broceliande, which proposes efficient implementation of APs and FPs to perform remote sensing image classification, with various choices of tree structures as well as attributes. We perform our experiments on two high resolution optical image data sets and provide comparative results of APs and FPs, showing and confirming their effectiveness to describe and classify remote sensing images.