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

Abstract

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.

Assessment of the Usability of SAR and Optical Satellite Data for Monitoring Spatio-Temporal Changes in Surface Water: Bodrog River Case Study

“…Additionally, the resulting extracted water surface masks may be affected by the mixing of multiple pixels near riverbanks. Specifically, the usage of VH polarisation configuration shows wider variability of backscattering in vegetation areas due to volume scattering, confirmed in these publications [77,78].…”

An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

“…where Y ∈ R m×n is the target clean image, ζ ∈ R m×n stands for multiplicative noise with assumed variance equal to σ 2 µ , and ⊙ represents point-wise multiplication. The image/noise model relies on general information about SAR image/speckle properties [48].…”

Optimal parameters selection of the Frost filter based on despeckling efficiency prediction for Sentinel SAR images

“…One possible way to increase despeckling efficiency is to select optimal parameters for a given filter taking into account image properties and speckle characteristics. Several methods have been proposed for selection of filter parameters [8][9][10]. In [8], adaptive windowing approach has been proposed where the window size is automatically adjusted according to image characteristics (homogeneous or heterogeneous regions) for local statistics Mean and Lee filters.…”

“…Our previous studies have shown that it is possible to predict despeckling efficiency beforehand with high accuracy for several filters [12]. We have already demonstrated how to select the window size for the well-known local statistics Lee filter based on despeckling efficiency prediction [10]. Therefore, we demonstrate here that such an approach can be extended to the Frost filter that has two adjustable parameters, with application to real-life Sentinel-1 SAR images in order to provide a better trade-off between speckle suppression and edge/detail preservation.…”

“…More in detail, despeckling efficiency is described by a value of improvement due to despeckling for a given image quality measure, i.e. a difference for a given image quality measure after and before applying despeckling [10,12]. Both PSNR and PSNR-HVS-M image quality measures are expressed in dB, where larger values correspond to better image quality.…”