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.
Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-1458 |
Date | 22 May 2006 |
Creators | Stein, Gregory W. |
Publisher | ScholarWorks@UNO |
Source Sets | University of New Orleans |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | University of New Orleans Theses and Dissertations |
Page generated in 0.002 seconds