Wavelet Based SAR Speckle Reduction and Image Compression

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameSPIE Symp. OE/Aerospace Sensing and Dual Use Photonics, Algorithm for Synthetic Aperture Radar Imageen_US
dc.citation.firstpage17en_US
dc.citation.lastpage21en_US
dc.citation.volumeNumber2en_US
dc.contributor.authorOdegard, Jan E.en_US
dc.contributor.authorGuo, Haitaoen_US
dc.contributor.authorLang, Markusen_US
dc.contributor.authorBurrus, C. Sidneyen_US
dc.contributor.authorWells, R.O.en_US
dc.contributor.authorNovak, L.M.en_US
dc.contributor.authorHiett, M.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.contributor.orgCML (http://cml.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:56:50Zen_US
dc.date.available2007-10-31T00:56:50Zen_US
dc.date.issued1995-04-01en_US
dc.date.modified2004-11-10en_US
dc.date.note2004-11-10en_US
dc.date.submitted1995-04-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractThis paper evaluates the performance of the recently published wavelet based algorithm for speckle reduction of SAR images. The original algorithm, based on the theory of wavelet thresholding due to Donoho and Johnstone, has been shown to improve speckle statistics. In this paper we give more extensive results based on tests performed at Lincoln Laboratory (LL). The LL benchmarks show that the SAR imagery is significantly enhanced perceptually. Although the wavelet processed data results in an increase in the number of natural clutter false alarms (from trees etc.) an appropriately modified CFAR detector (i.e., by clamping the estimated clutter standard deviation) eliminates the extra false alarms. The paper also gives preliminary results on the performance of the new and improved wavelet denoising algorithm based on the shift invariant wavelet transform. By thresholding the shift invariant discrete wavelet transform we can further reduce speckle to achieve a perceptually superior SAR image with ground truth information significantly enhanced. Preliminary results on the speckle statistics of this new algorithm is improved over the classical wavelet denoising algorithm. Finally, we show that the classical denoising algorithm as proposed by Donoho and Johnstone and applied to SAR has the added benefit of achieving about 3:1 compression with essentially no loss in image fidelity.en_US
dc.identifier.citationJ. E. Odegard, H. Guo, M. Lang, C. S. Burrus, R. Wells, L. Novak and M. Hiett, "Wavelet Based SAR Speckle Reduction and Image Compression," vol. 2, 1995.en_US
dc.identifier.doihttp://dx.doi.org/10.1117/12.210843en_US
dc.identifier.urihttps://hdl.handle.net/1911/20171en_US
dc.language.isoengen_US
dc.subjectTemporaryen_US
dc.subject.keywordTemporaryen_US
dc.subject.otherWavelet based Signal/Image Processingen_US
dc.titleWavelet Based SAR Speckle Reduction and Image Compressionen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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