Yang Xiao-hui, Jin Hai-yan, Jiao Li-cheng. SAR Speckle Reduction Based on Generalized Cross Validation and Cycle Spinning[J]. Journal of Electronics & Information Technology, 2007, 29(8): 1779-1783. doi: 10.3724/SP.J.1146.2006.01071
Citation:
Yang Xiao-hui, Jin Hai-yan, Jiao Li-cheng. SAR Speckle Reduction Based on Generalized Cross Validation and Cycle Spinning[J]. Journal of Electronics & Information Technology, 2007, 29(8): 1779-1783. doi: 10.3724/SP.J.1146.2006.01071
Yang Xiao-hui, Jin Hai-yan, Jiao Li-cheng. SAR Speckle Reduction Based on Generalized Cross Validation and Cycle Spinning[J]. Journal of Electronics & Information Technology, 2007, 29(8): 1779-1783. doi: 10.3724/SP.J.1146.2006.01071
Citation:
Yang Xiao-hui, Jin Hai-yan, Jiao Li-cheng. SAR Speckle Reduction Based on Generalized Cross Validation and Cycle Spinning[J]. Journal of Electronics & Information Technology, 2007, 29(8): 1779-1783. doi: 10.3724/SP.J.1146.2006.01071
Considering the statistical characteristics of SAR images, a novel speckle reduction algorithm is presented in this paper. This technique is by virtue of generalized cross validation and constructs an object function to acquire the asymptotic optimal threshold without of estimating noise variance. After applying the wavelet shrinkage on SAR image, cycle spinning strategy is introduced to wipe off the visible ringing effects along the edges. Numerical tests show that the proposed SAR speckle reduction algorithm provides improvements both in visual effects and quantitative analysis, which can smooth image effectively and remain the edges and texture clearly.
Kuan D T and Sawchuk A A. Adaptive noise smoothing filter for signal-dependent noise[J].IEEE Trans. on Pattern Anal. and Machine Intel.1985, 7(2):165-177[2]Frost V S and Stiles J A. A model for radar images and its application to adaptive digital filtering of multiplicative noise[J].IEEE Trans. on Pattern Anal. and Machine Intel.1982, 4(2):157-165[3]Lee J S. A simple speckle smoothing algorithm for synthetic aperture radar images. IEEE Trans. on System, Man, and Cybernatics, 1983, 13(1): 85-89.[4]Lopes A, Nezry E, and Touzi R, et al.. Maximum a posteriori filtering and first order texture models in SAR images. Proc. of IEEE Int, Geoscience and Remote Sensing Symposium, Washington, D C, 1990: 2409-2412.[5]Donoho D L. Denoising by soft-thresholding[J].IEEE Trans. Inform. Theory.1995, 41(3):613-627[6]Min D, Cheng P, Andrew K C, and Dmitri L. Bayesian wavelet shrinkage with edge detection for SAR image despeckling[J].IEEE Trans. on Geosc. and Remote Sensing.2004, 42(8):1642-1648[7]Helmi Z, Shafri M, and Paul M M. Wavelet shrinkage in noise removal of hyperspectral remote sensing data[J].American Journal of Applied Sciences.2005, 2(7):1169-1173[8]Jansen M, Malfait M, and Bultheel A. Generalization cross validation for wavelet thresholding[J].Signal Process.1997, 56(1):33-44[9]Weyrich N and Warhola G T. Wavelet shrinkage and generalized cross validation for image denoising[J].IEEE Trans. on Image Processing.1998, 7(1):82-90[10]Coifman R R and Donoho D L. Translation invariant denoising. Wavelets and Statistics. New York/Berlin: Springer-Verlag, 1995: 125-150.