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

Haiyan Gu, Haitao Li, Li Yan, Zhengjun Liu, Thomas Blaschke, Uwe Soergel
2017 Remote Sensing  
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
more » ... tology 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. software packages; peer-reviewed journal papers; six highly successful biennial international GEOBIA conferences; and a growing number of books and university theses [7] [8] [9]. A GEOBIA wiki is used to promote international exchange and development [9]. GEOBIA is a hot topic in RS and GIS [1, 8] and has been widely applied in global environmental monitoring, agricultural development, natural resource management, and defence and security [10] [11] [12] [13] [14] . It has been recognized as a new paradigm in RS and GIS [15] . Ontology originated in Western philosophy and was then introduced into GIS [16]. The concept of domain knowledge is expressed in the form of machine-understandable rulesets and is utilised for semantic modelling, semantic interoperability, knowledge sharing and information retrieval services in the field of GIS [16] [17] [18] . Recently, researchers have begun to attach importance to the application of ontology in the field of remote sensing, especially in remote sensing image interpretation. Arvor et al. (2013) described how to utilise ontology experts' knowledge to improve the automation of image processing and analysis the potential applications of GEOBIA, which can provide theoretical support for remote sensing data discovery, multi-source data integration, image interpretation, workflow management and knowledge sharing [19]. Jesús et al. (2013) built a framework for ocean image classification based on ontologies, which describes how to build ontology model for low and high level of features, classifiers and rule-based expert systems [20]. Dejrriri et al. (2012) presented GEOBIA and data mining techniques for non-planned city residents based on ontology [21]. Kohli et al. (2012) provided a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification [22]. Forestier et al. (2013) built a coastal zone ontology to extract coastal zones using background and semantic knowledge [23]. Kyzirakos et al. (2014) provided wildfire monitoring services by combining satellite images and geospatial data with ontologies [24]. Belgiu et al. (2014a) presented an ontology-based classification method for extracting types of buildings where airborne laser scanning data are employed and obtained effective recognition results [25]. Belgiu et al. (2014b) provided a formal expression tool to express object-based image analysis technology through ontologies [26]. Cui (2013) presented a GEOBIA method based on geo-ontology and relative elevation [27]. Luo (2016) developed an ontology-based framework that was used to extract land cover information while interpreting HRS remote sensing images at the regional level [28] . Durand et al. (2007) proposed a recognition method based on an ontology which has been developed by experts from the particular domain [29] . Bannour et al. (2011) presented an overview and an analysis of the use of semantic hierarchies and ontologies to provide a deeper image understanding and a better image annotation in order to furnish retrieval facilities to users [30] . Andres et al. (2012) demonstrate that expert knowledge explanation via ontologies can improve automation of satellite image exploitation [31] . All these studies focus either on a single thematic aspect based on expert knowledge or on a specific geographic entity. However, existing studies do not provide comprehensive and transferable frameworks for objective modelling in GEOBIA. None of the existing methods allows for a general ontology driven semantic classification method. Therefore, this study develops an object-based semantic classification methodology for high resolution remote sensing imagery using ontology that enables a common understanding of the GEOBIA framework structure for human operators and for software agents. This methodology shall enable reuse and transferability of a general GEOBIA ontology while making GEOBIA assumptions explicit and analysing the GEOBIA knowledge corpus. Methodology The workflow of the object-based semantic classification is organized as follows: in the ontologymodel building step, land-cover models, image object features and classifiers are generated using the procedure described in Section 2.2 (Step 1, Figure 1) . The result is a semantic network model. Subsequently, the remote sensing image is classified using a machine learning method and the initial classification result is imported into the semantic network model (Step 2, Figure 1 ), which is described in Section 2.3. In the last step, the initial classification result is reclassified and validated to get Remote Sens. 2017, 9, 329 3 of 21 the final classification result based on the semantic rules (Step 3, Figure 1 ), which is described in Section 2.4. The semantic network model is the interactive file between the initial classification and the semantic classification.
doi:10.3390/rs9040329 fatcat:lvhb5yrukzdfvdc6zzjil57g3a