Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/13377
Title: Development and analysis of wavelet based speckle reduction algorithms for synthetic aperture radar images
Researcher: Jennifer Ranjani J
Guide(s): Thiruvengadam, S.J.
Keywords: Speckle reduction, wavelet, synthetic aperture radar, dual tree complex wavelet transform, ratio of exponential weighted averages, figure of merit
Upload Date: 28-Nov-2013
University: Anna University
Completed Date: 
Abstract: There has been a growing interest in Synthetic Aperture Radar (SAR) imaging due to its ability to operate under varied weather conditions. SAR is also capable of penetrating through cloud and soil. SAR systems have its importance in a variety of applications such as remote sensing for mapping search-and-scene rescue, mine detection, and target recognition. In this thesis, Dual Tree Complex Wavelet Transform(DTCWT) based despeckling algorithm is proposed for SAR images, considering the significant dependencies of the coefficients across different scales. In this thesis, a method is proposed to reduce the number of false edges in the MRGoA detector output. This algorithm discriminates the objects and the false edges automatically by applying the principle of component connectivity and Shannon s entropy. Further, an automatic threshold selection method is also proposed for the existing Ratio of Exponential Weighted Averages (ROEWA) based edge detector. Otsu s nonparatmetric and unsupervised principle is used for automatic threshold selection in the ROEWA edge detector. In the proposed method, optimal threshold is selected by maximizing the seperability of the classes in gray level by incorporating a simple search strategy. From the experimental results, it is observed that the automatic threshold selection methods proposed for MRGoA and ROEWA has better edge detection capability. The Pratt s Figure of Merit (FOM) performance of the edge detection algorithms is improved when speckle reduction is done prior to edge detection. FOM value of the proposed entropy based MRGoA is increased by 5% when compared to the existing MRGoA edge detector. Also, edge detection by the automatic threshold selection based ROEWA is more efficient than ROEWA with predefined thresholds. newline newline newline
Pagination: xii, 159
URI: http://hdl.handle.net/10603/13377
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File78.62 kBAdobe PDFView/Open
02_certificates.pdf1.38 MBAdobe PDFView/Open
03_abstract.pdf66.08 kBAdobe PDFView/Open
04_acknowledgement.pdf51.71 kBAdobe PDFView/Open
05_contents.pdf108.01 kBAdobe PDFView/Open
06_chapter 1.pdf263.67 kBAdobe PDFView/Open
07_chapter 2.pdf3.39 MBAdobe PDFView/Open
08_chapter 3.pdf2.82 MBAdobe PDFView/Open
09_chapter 4.pdf1.4 MBAdobe PDFView/Open
10_chapter 5.pdf84.85 kBAdobe PDFView/Open
11_references.pdf132.68 kBAdobe PDFView/Open
12_publications.pdf62.1 kBAdobe PDFView/Open
13_vitae.pdf47.43 kBAdobe PDFView/Open


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