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Open AccessArticle

Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data

1
Department of Information Engineering, University of Florence, 50139 Florence, Italy
2
Institute of Applied Physics “Nello Carrara”, Research Area of Florence, 50019 Sesto Fiorentino (FI), Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 331; https://doi.org/10.3390/rs12020331
Received: 4 December 2019 / Revised: 9 January 2020 / Accepted: 11 January 2020 / Published: 20 January 2020
In this work, we extended a procedure for the spatial decorrelation of fully-developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. The spatial correlation of the noise depends on the tapering window in the Fourier domain used by the SAR processor to avoid defocusing of targets caused by Gibbs effects. Since each polarimetric channel is focused independently of the others, the noise-whitening procedure can be performed applying the decorrelation stage to each channel separately. Equivalently, the noise-whitening stage is applied to each element of the scattering matrix before any multilooking operation, either coherent or not, is performed. In order to evaluate the impact of a spatial decorrelation of the noise on the performance of polarimetric despeckling filters, we make use of simulated PolSAR data, having user-defined polarimetric features. We optionally introduce a spatial correlation of the noise in the simulated complex data by means of a 2D separable Hamming window in the Fourier domain. Then, we remove such a correlation by using the whitening procedure and compare the accuracy of both despeckling and polarimetric features estimation for the three following cases: uncorrelated, correlated, and decorrelated images. Simulation results showed a steady improvement of performance scores, most notably the equivalent number of looks (ENL), which increased after decorrelation and closely attained the value of the uncorrelated case. Besides ENL, the benefits of the noise decorrelation hold also for polarimetric features, whose estimation accuracy is diminished by the correlation. Also, the trends of simulations were confirmed by qualitative results of experiments carried out on a true Radarsat-2 image. View Full-Text
Keywords: noise whitening; polarimetric features; polarimetric synthetic aperture radar (PolSAR); speckle filtering; statistical estimation noise whitening; polarimetric features; polarimetric synthetic aperture radar (PolSAR); speckle filtering; statistical estimation
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MDPI and ACS Style

Arienzo, A.; Argenti, F.; Alparone, L.; Gherardelli, M. Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data. Remote Sens. 2020, 12, 331. https://doi.org/10.3390/rs12020331

AMA Style

Arienzo A, Argenti F, Alparone L, Gherardelli M. Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data. Remote Sensing. 2020; 12(2):331. https://doi.org/10.3390/rs12020331

Chicago/Turabian Style

Arienzo, Alberto; Argenti, Fabrizio; Alparone, Luciano; Gherardelli, Monica. 2020. "Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data" Remote Sens. 12, no. 2: 331. https://doi.org/10.3390/rs12020331

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