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This paper introduces the interpretation meta knowledge model devised for the InterCloud platform. InterCloud is a remote sensing image interpretation platform designed to run on computer clusters or on cloud computing infrastructure. The system is capable of distributing data processing tasks, such as segmentation, feature extraction and classification procedures over the processing elements of a computer grid in a transparent way to the user. Moreover, InterCloud can exploit the potential scalability offered by commercial cloud computing infrastructure services, enabling the interpretation of very large remote sensing datasets in an efficient way. The proposed meta model comprises two types of knowledge: declarative and procedural. The former describes the characteristics of the classes of objects expected to be found in the scene to be interpreted, and the relationships among those classes. The latter describes the functions and procedures that should be applied over the data in order to achieve the desired interpretation. In the proposed knowledge model, the user expresses declarative knowledge through the definition of an ontology, so-called descriptive ontology, which conveys the formal naming and definition of the properties and interrelationships of the object classes in a particular application. Procedural knowledge is expressed by the so-called task ontology, which is represented by a directed graph, in which the nodes represent operations over the input images or over the segments generated by segmentation operations. Besides segmentation, crisp or fuzzy classification operations can be defined by the user. The graph edges define the data flow between operations, which are triggered by the control process as soon as their inputs are produced by the preceding operations. In this paper we illustrate the main components of the meta knowledge model through a theoretical application.

An object-based meta knowledge model for a distributed image interpretation system