Semantics-Enabled Framework for Knowledge Discovery from Earth Observation Data Archives


Earth observation data has increased significantly over the last decades with satellites collecting and transmitting to Earth receiving stations several terabytes of data a day. This data acquisition rate is a major challenge to the existing data exploitation and dissemination approaches. The lack of content and semantic based interactive information searching and retrieval capabilities from the image archives is an impediment to the use of the data. This presentation will describe a NASA and NOAA funded framework being developed at Mississippi State University that is built around a concept-based model using domain-dependant ontologies. In this framework, the basic concepts of the domain are identified first and generalized later, depending upon the level of reasoning required for executing a particular query. The approach employs an unsupervised segmentation algorithm to extract homogeneous regions and calculate primitive descriptors for each region based on color, texture and shape. Then, an unsupervised classification is performed by means of a Kernel Principal Components Analysis (KPCA) method, which extracts components of features that are nonlinearly related to the input variables, followed by a Support Vector Machine (SVM) classification to generate models for the object classes. The assignment of concepts in the ontology to the objects is achieved automatically by the integration of a Description Logic (DL) based inference mechanism, which processes the interrelationships between the properties held in the specific concepts of the domain ontology. The framework is exercised in a coastal zone domain. Special emphasis will be on the "hard problems" associated with semantic ontology reasoning.