Ontologies are considered as one of the most powerful tools for knowledge representation and reasoning. Thus, they are considered as a fundamental support for image annotation, indexing and retrieval. In order to build a remote sensing satellite image ontology that models the geographic objects that we find in a scene, their characteristics as well as their relationships, we propose to reuse existing geographic ontologies to enrich an ontological core. Reusing high quality resources (called source ontologies) helps ensuring a good quality for the extracted knowledge, and alleviating the conceptualization stage, i.e. avoiding building a new ontology from scratch. Ontology alignment is an important phase within the enrichment process. It is a process that allows discovering mappings between core and source ontologies, where each mapping is a couple of entities brought from each ontology and linked together either by an equivalence or a subsumption relationship. Such relationships are based on various similarity measures. In this paper, we first present a brief literature review of existing theoretical frameworks for similarity measures, then we describe a new alignment approach based on a semi-automatic mapping selection process that needs little human intervention. First experiments show the benefit from using the proposed approach.