<p>Understanding high spatial resolution (HSR) remote sensing (RS) imagery requires exploring geo-objects and their geographic relationships. This area has grabbed the interest of the RS community since it delivers more specific information than typical tasks like classification and object recognition. Despite recent advancements in RS image description generation, it remains challenging to characterize the RS image in terms of geographic relationships between the objects included in it. This work proposes a method for semantic understanding of high spatial resolution RS images by identifying topological, directional, and proximity geospatial relationships between geo-objects and representing these relationships in the form of sentences. The proposed methodology began with the detection of RS image objects in the form of oriented bounding boxes (OBB). The dimensionality extended 9-intersection model (DE9IM) was then used to identify topological relations. Directional relationships are then identified using the centroid of both objects, and proximity relations are identified based on the distance between two objects. Thus, between each pair of objects, all three types of relations are constructed, and relation triplets have been created for each relation. To decrease the redundancy in the relationships, a hierarchy of relations is proposed, and an extended region connection calculus (ERCC) for nesting of relations is constructed. ERCC is generated by linking directional and proximity relations with topological relations, and three levels of relations are designed. The methodology is validated using a 34-class RS image dataset constructed for object detection in the form of oriented bounding boxes.</p>