Impact of a Spatial Decorrelation of the Noise on the Performance of Despeckling Filters for Polarimetric SAR Data
In this work, we have extended a procedure for the spatial decorrelation of fully developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. Assuming that the SAR processor is the same for all the polarimetric channels, the noise-whitening procedure can...
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Published in | 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) pp. 1113 - 1121 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.06.2019
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Subjects | |
Online Access | Get full text |
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Summary: | In this work, we have extended a procedure for the spatial decorrelation of fully developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. Assuming that the SAR processor is the same for all the polarimetric channels, 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 speckle decorrelation on the performance of classical polarimetric despeckling filters, we make use of simulated PolSAR data. We optionally introduce a spatial correlation of the noise in the simulated data by means of a 2D separable Hamming window; then we remove such a correlation by using the whitening procedure and compare the despeckling performance for the three following cases: uncorrelated, correlated, and whitened correlated SLC images. Simulation results show a steady improvement of performance scores, e.g., ENL, which are better after decorrelation than those measured with correlated noise and closely attain those of the uncorrelated case. Also, qualitative results on a true RADARSAT-2 image confirm the trend of the simulated data. |
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ISSN: | 2694-5053 |
DOI: | 10.1109/PIERS-Spring46901.2019.9017856 |