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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Stein, Gregory W."],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Charalampidis, Dimitrios","Bourgeois, Edit","Chen, Huimin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006-05-22T07:00:00Z","date_published":"2006-05-22T07:00:00Z","updated_at":"2026-07-24T05:28:22Z","subjects":["Target Detection","Wavelet","Fractal"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/437","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Charalampidis, Dimitrios","Bourgeois, Edit","Chen, Huimin"]},{"key":"dc:creator","label":"Author","values":["Stein, Gregory W."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Target Detection","Wavelet","Fractal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/437"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Target Detection Using a Wavelet-Based Fractal Scheme"]}]}],"canonical_facts":{"dc:contributor":["Charalampidis, Dimitrios","Bourgeois, Edit","Chen, Huimin"],"dc:creator":["Stein, Gregory W."],"dc:description.abstract":["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.
"],"dc:identifier":["https://scholarworks.uno.edu/td/437"],"dc:subject":["Target Detection","Wavelet","Fractal"],"dc:title":["Target Detection Using a Wavelet-Based Fractal Scheme"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:28:22Z"}