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Article

Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images

1
Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkiv, Ukraine
2
Computational Imaging Group, Tampere University, 33720 Tampere, Finland
*
Author to whom correspondence should be addressed.
Academic Editor: Alexandru Isar
Remote Sens. 2021, 13(10), 1887; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101887
Received: 31 March 2021 / Revised: 29 April 2021 / Accepted: 6 May 2021 / Published: 12 May 2021
(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)
Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window or block size, thresholds, etc.). The local statistic Lee filter is one of the most popular and best-known despeckling techniques in radar image processing. Using this filter and Sentinel-1 images as a case study, we show how filter parameters, namely scanning window size, can be selected for a given image based on filter efficiency prediction. Such a prediction can be carried out using a set of input parameters that can be easily and quickly calculated and employing a trained neural network that allows determining one or several criteria of filtering efficiency with high accuracy. The statistical analysis of the obtained results is carried out. This characterizes improvements due to the adaptive selection of the filter window size, both potential and based on prediction. We also analyzed what happens if, due to prediction errors, erroneous decisions are undertaken. Examples for simulated and real-life images are presented. View Full-Text
Keywords: image quality assessment; visual quality metrics; neural networks; despeckling; Sentinel-1 image quality assessment; visual quality metrics; neural networks; despeckling; Sentinel-1
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MDPI and ACS Style

Rubel, O.; Lukin, V.; Rubel, A.; Egiazarian, K. Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images. Remote Sens. 2021, 13, 1887. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101887

AMA Style

Rubel O, Lukin V, Rubel A, Egiazarian K. Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images. Remote Sensing. 2021; 13(10):1887. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101887

Chicago/Turabian Style

Rubel, Oleksii, Vladimir Lukin, Andrii Rubel, and Karen Egiazarian. 2021. "Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images" Remote Sensing 13, no. 10: 1887. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101887

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