A Fuzzy Spatial Reasoner for Multi-Scale GEOBIA Ontologies

In Geographic Object-Based Image Analysis (GEOBIA) an image is partitioned into objects by a segmentation algorithm. These objects are then classified into semantic categories based on unsupervised/supervised methods, or knowledge-based methods, such as an ontology. The aim of this paper was to develop a SPatial Ontology Reasoner (SPOR) to allow the development of GEOBIA ontologies by employing fuzzy, spatial, and multi-scale representations, with time efficiency. An enhanced version of the Web Ontology Language 2 (OWL2) with fuzzy representations was adopted and expanded to represent fuzzy spatial relationships within the framework of GEOBIA . Segmentation results are stored within PostgreSQL. An ontology described the class/subclass hierarchy and class definitions. SPOR integrated PostgreSQL and the ontology, to classify the objects. To demonstrate the framework, a QuickBird image was employed for building extraction. Accuracy assessment indicated that 87 percent of building rooftops were detected.