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Target Detection Using a Wavelet-Based Fractal Scheme

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.

Identiferoai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-1458
Date22 May 2006
CreatorsStein, Gregory W.
PublisherScholarWorks@UNO
Source SetsUniversity of New Orleans
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceUniversity of New Orleans Theses and Dissertations

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