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.
2012
IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS
2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Monaco di Baviera, Germania
22-27 July 2014
L.Alparone;F.Argenti;T.Bianchi;A.Lapini;B.Aiazzi;S.Baronti;C.D'Elia;S.Ruscino
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/780354
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