This paper describes the most recent achievements in speckle reduction of COSMO-SkyMed (CSK R ) synthetic aperture radar (SAR) data. An advanced multresolution despeckling filter, based on undecimated wavelet transform (UDWT) and maximum a-posteriori (MAP) estimation has been specialized and optimized to CSK R data, both single- and multi-look. The tradeoff between performances and computational complexity has been investigated: Laplacian-Gaussian and generalized Gaussian (GG) priors for MAP estimation in UDWT domain differ by one order of magnitude in computation cost. Pre-processing of point targets and segmentation of wavelet planes has been exploited to effectively handle the heterogeneity of the data. Besides traditional supervised methods to evaluate the quality of despeckling, a novel procedure, fully automated, based on bivariate analysis of noisy and denoised image has been devised.
Multiresolution map despeckling of COSMO-SkyMed images / L.Alparone;F.Argenti;T.Bianchi;A.Lapini;B.Aiazzi;S.Baronti;C.D'Elia;S.Ruscino. - STAMPA. - (2012), pp. 5490-5493. (Intervento presentato al convegno 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) tenutosi a Monaco di Baviera, Germania nel 22-27 July 2014) [10.1109/IGARSS.2012.6352363].
Multiresolution map despeckling of COSMO-SkyMed images
ALPARONE, LUCIANO;ARGENTI, FABRIZIO;LAPINI, ALESSANDRO;
2012
Abstract
This paper describes the most recent achievements in speckle reduction of COSMO-SkyMed (CSK R ) synthetic aperture radar (SAR) data. An advanced multresolution despeckling filter, based on undecimated wavelet transform (UDWT) and maximum a-posteriori (MAP) estimation has been specialized and optimized to CSK R data, both single- and multi-look. The tradeoff between performances and computational complexity has been investigated: Laplacian-Gaussian and generalized Gaussian (GG) priors for MAP estimation in UDWT domain differ by one order of magnitude in computation cost. Pre-processing of point targets and segmentation of wavelet planes has been exploited to effectively handle the heterogeneity of the data. Besides traditional supervised methods to evaluate the quality of despeckling, a novel procedure, fully automated, based on bivariate analysis of noisy and denoised image has been devised.File | Dimensione | Formato | |
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