Actually, we still have not a well established satellite image ontology that would be very useful to assist us to study major facts affecting earth, detect and monitor natural phenomena. Nevertheless, there are several domain-specific geographic ontologies that can be used as semantic resources to build such an ontology. Thus, we can start from a core satellite image ontology such as the one of Durand and enrich it using these geographic ontologies. Ontology matching is one of the principal activities in the ontology enrichment process and highly depends on the similarity measures that are considered as well as the way they are combined together in order to decide whether two concepts coming from different ontologies are alienable. In the literature, research on similarity mainly focuses on issues related to how to compute and refine similarity measures. However, few research addresses studying their dependencies and contributions in the evaluation of the overall similarity between objects to be compared. In this paper and in order to align an initial remote sensing satellite image ontology with a set of geographic ontologies, we give insights on the main similarity models as well as their associated measures. We then propose a method in order to select a reduced set of the most important similarity measures to use for the alignment. Afterwards, we present a method that can produce a ranking model that allows sorting mappings between concepts coming from two different ontologies, in a decreasing order of a global similarity score. First experimentations show that the proposed approach is promising.