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.