An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology

作者：Gu, Haiyan^*; Li, Haitao; Yan, Li; Liu, Zhengjun; Blaschke, Thomas; Soergel, Uwe

来源：Remote Sensing, 2017, 9(4): 329.

DOI：10.3390/rs9040329

• geographic object-based image analysis
• ontology
• semantic network model
• web ontology language
• semantic web rule language
• machine learning
• semantic rule
• land-cover classification

Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIA-similar to other emerging paradigms-lacks formal expressions and objective modelling structures and in particular semantic classification methods using ontologies. This study has put forward an object-based semantic classification method for high resolution satellite imagery using an ontology that aims to fully exploit the advantages of ontology to GEOBIA. A three-step workflow has been introduced: ontology modelling, initial classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic rules. The classification part is based on data-driven machine learning, segmentation, feature selection, sample collection and an initial classification. Then, image objects are re-classified based on the ontological model whereby the semantic relations are expressed in the formal languages OWL and SWRL. The results show that the method with ontology-as compared to the decision tree classification without using the ontology-yielded minor statistical improvements in terms of accuracy for this particular image. However, this framework enhances existing GEOBIA methodologies: ontologies express and organize the whole structure of GEOBIA and allow establishing relations, particularly spatially explicit relations between objects as well as multi-scale/hierarchical relations.

• 出版日期2017-4
• 单位武汉大学; 中国测绘科学研究院