Date of Award

Fall 12-2007

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computing

Committee Chair

Dr. Dia Ali

Committee Chair Department

Computing

Committee Member 2

Dr. Adel Ali

Committee Member 2 Department

Computing

Committee Member 3

Dr. Chaoyang Zhang

Committee Member 3 Department

Computing

Committee Member 4

Dr. Khaled El-Sawi

Committee Member 4 Department

Computing

Committee Member 5

Dr. Beddhu Murali

Committee Member 5 Department

Computing

Committee Member 6

Dr. Ras Pandey

Committee Member 6 Department

Physics and Astronomy

Abstract

An integrated approach of feature analysis - Directional Edge Analytical Model (DEAM) is presented. Remote sensing and image processing with feature extraction are significant for collecting information. Many different techniques, invariant to different types of geometric transformations, contextual conditions, and task demands, have been developed for recognizing features of objects. Most of the current feature analysis has emphasized non-remote objects and has lacked handling with the properties of remote sensing images. In addition, Relational structures are particularly suitable for representing complex features and for providing matching solutions; this area of research is still in very early stage.

Using directional edges as descriptors for relational features, DEAM provides supervised models with matching procedures. Novel methods with adaptive threshold detectors and directional descriptors are designed according to the properties of remote sensing images. DEAM analyzes the connection of the functional edges in an object, and then it builds the analytical model by feature descriptors. It utilizes “many to many relations” with “relation of relations” to reduce the redundancy and to increase the accuracy of a training set. After feature relational mapping, the corresponding descriptors between the model and the target will assist researchers identifying the object. DEAM demonstrate superiority in quantifying structural relationships and in normalized features that are desired for many analytical applications.

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