Date of Award


Degree Type


Degree Name


Degree Program



Electrical Engineering

Major Professor

Charalampidis, Dimitrios

Second Advisor

Bourgeois, Edit

Third Advisor

Chen, Huimin

Fourth Advisor

Jilkov, Vesselin


In this thesis, a target detection technique using a rotational invariant wavelet-based scheme is presented. The technique is evaluated on Synthetic Aperture Rader (SAR) imaging and compared with a previously developed fractal-based technique, namely the extended fractal (EF) model. Both techniques attempt to exploit the textural characteristics of SAR imagery. Recently, a wavelet-based fractal feature set, similar to the proposed one, was compared with the EF feature for a general texture classification problem. The wavelet-based technique yielded a lower classification error than EF, which motivated the comparison between the two techniques presented in this paper. Experimental results show that the proposed techniques feature map provides a lower false alarm rate than the previously developed method.


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