Advisor

King, Roger L.

Committee Member

Shaw, David

Committee Member

Banicescu, Ioana

Date of Degree

1-1-2006

Document Type

Dissertation - Open Access

Major

Computer Engineering

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Earth observation data has increased significantly over the last decades with satellites collecting and transmitting to Earth receiving stations in excess of three 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 semantics based interactive information searching and retrieval capabilities from the image archives is an impediment to the use of the data. The proposed framework (Intelligent Interactive Image Knowledge retrieval-I3KR) is built around a concept-based model using domain dependant ontologies. An unsupervised segmentation algorithm is employed to extract homogeneous regions and calculate primitive descriptors for each region. An unsupervised classification by means of a Kernel Principal Components Analysis (KPCA) method is then performed, 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 the concepts in the ontology to the objects is achieved by a Description Logics (DL) based inference mechanism. This research also proposes new methodologies for domain-specific rapid image information mining (RIIM) modules for disaster response activities. In addition, several organizations/individuals are involved in the analysis of Earth observation data. Often the results of this analysis are presented as derivative products in various classification systems (e.g. land use/land cover, soils, hydrology, wetlands, etc.). The generated thematic data sets are highly heterogeneous in syntax, structure and semantics. The second framework developed as a part of this research (Semantics-Enabled Thematic data Integration (SETI)) focuses on identifying and resolving semantic conflicts such as confounding conflicts, scaling and units conflicts, and naming conflicts between data in different classification schemes. The shared ontology approach presented in this work facilitates the reclassification of information items from one information source into the application ontology of another source. Reasoning on the system is performed through a DL reasoner that allows classification of data from one context to another by equality and subsumption. This enables the proposed system to provide enhanced knowledge discovery, query processing, and searching in way that is not possible with key word based searches.

URI

https://hdl.handle.net/11668/19129

Comments

Remote Sensing||Ontology||Support vector machines||Semantics||Middleware||Thematic||Land Cover||Semantic Web||Machine Learning

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