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Volume: 32 | Article ID: art00032
Prediction of Lee filter performance for Sentinel-1 SAR images
  DOI :  10.2352/ISSN.2470-1173.2020.9.IQSP-371  Published OnlineJanuary 2020

Synthetic aperture radar (SAR) images are corrupted by a specific noise-like phenomenon called speckle that prevents efficient processing of remote sensing data. There are many denoising methods already proposed including well known (local statistic) Lee filter. Its performance in terms of different criteria depends on several factors including image complexity where it sometimes occurs useless to process complex structure images (containing texture regions). We show that performance of the Lee filter can be predicted before starting image filtering and which can be done faster than the filtering itself. For this purpose, we propose to apply a trained neural network that employs analysis of image statistics and spectral features in a limited number of scanning windows. We show that many metrics including visual quality metrics can be predicted for SAR images acquired by Sentinel-1 sensor recently put into operation.

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Oleksii Rubel, Vladimir Lukin, Andrii Rubel, Karen Egiazarian, "Prediction of Lee filter performance for Sentinel-1 SAR imagesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XVII,  2020,  pp 371-1 - 371-7,

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