Semantics-enabled framework for knowledge discovery from Earth observation data archives

Earth observation data have increased significantly over the last decades with satellites collecting and transmitting to Earth receiving stations in excess of 3 TB 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. In this paper, we describe a framework we have developed [Intelligent Interactive Image Knowledge Retrieval (I/sup 3/KR)] 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. We employ an unsupervised segmentation algorithm to extract homogeneous regions and calculate primitive descriptors for each region based on color, texture, and shape. We initially perform an unsupervised classification by means of a kernel principal components analysis method, which extracts components of features that are nonlinearly related to the input variables, followed by a support vector machine 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 logics-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.