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PolSAR image segmentation has long been an important problem in the PolSAR remote sensing community. Many segmentation algorithms describe images in terms of a hierarchy of regions has attracted particular attention in recent years. However, they often contain more data than is required for an efficient description. In this paper, we propose an effective measure to extract hierarchical semantic structures...
Petri net has become one of the major formal methods for modeling and analyzing business processes. Petri net is utilized for business process modeling in inter-domain network management system. However business process models may contain errors. So model verification is of necessity and importance. A new model verification method is proposed based on incidence matrix of Petri net. It can be used...
The proposal and development of semantic web make ontology, as a new knowledge organization and semantic description method, get attention in various fields. The building of ontology is a basic and important task in semantic web. The construction method and organizational structure of ontology will directly affect the application of ontology. Based on the analysis of hierarchical characters existing...
Ontology Model and Semantic Link Network (SLN) are two important semantic data models in Semantic Web. Ontology Model can describe network resource in concept space, while SLN can describe various links and relations between network resources. Combination of Ontology Model and SLN can help to improve the effect of network resource retrieval, but data transfer and exchange between ontological knowledge...
In this paper, we present a fast approach to obtain semantic scene segmentation with high precision. We employ a two-stage classifier to label all image pixels. First, we use the regularized logistic regression to combine different appearance-based features and the improved spatial layout of labeling information. In the second stage, we incorporate the local, regional and global cues into a conditional...
The paper proposes a fast and accurate semantic segmentation approach for a large Polarimetric SAR (PolSAR) image using Conditional Random Fields (CRFs). It efficiently incorporates the polarimetric signatures, texture and intensity features into a unite CRFs model, and employs a fast max-margin training method for parameters learning. Experiments on RadarSat-2 PolSAR data in Flevoland test site demonstrate...
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