Image interpretation is a major topic in the remote sensing community. With the increasing acquisition of high spatial resolution (HSR) remotely sensed images, incorporating geographic object-based image analysis (GEOBIA) is becoming an important sub-discipline for improving remote sensing applications. The idea of integrating the human ability to understand images inspires research related to introducing expert knowledge into image object–based interpretation. The relevant work involved three parts: (1) identification and formalization of domain knowledge; (2) image segmentation and feature extraction; and (3) matching image objects with geographic concepts. This paper presents a novel way that combines multi-scaled segmented image objects with geographic concepts to express context in an ontology-guided image interpretation. Spectral features and geometric features of a single object are extracted after segmentation and topological relationships are also used in the interpretation. Web ontology language–query language (OWL-QL) formalize domain knowledge. Then the interpretation matching procedure is implemented by the OWL-QL query-answering. Compared with a supervised classification, which does not consider context, the proposed method validates two HSR images of coastal areas in China. Both the number of interpreted classes increased (19 classes over 10 classes in Case 1 and 12 classes over seven in Case 2), and the overall accuracy improved (0.77 over 0.55 in Case 1 and 0.86 over 0.65 in Case 2). The additional context of the image objects improved accuracy during image classification. The proposed approach shows the pivotal role of ontology for knowledge-guided interpretation.
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