Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images
Abstract
:1. Introduction
- Filter performance depends upon many factors, including the parameter settings used. Parameters can be varied and set depending on the filter type. These parameters include scanning window size [12,13], thresholds [17,18,25], a block size [25], parameters of variance stabilizing transforms [17,18], and the number of blocks processed jointly within nonlocal despeckling approaches [17,20].
- Despeckling (denoising) performance considerably depends on image properties. For simpler structure images (that contain large homogeneous regions), a better performance is usually achieved compared to complex structure images (which contain a lot of edges, small-sized objects and textures) [26,27,28].
- Speckle properties also influence a filter performance. There are filters applicable to speckle with a probability density function (PDF) close to Gaussian but there is no such restriction for some other filters. The spatial correlation of the speckle (and noise in general) plays a key role in the efficiency of its suppression [29,30]. This means that the spatial correlation of the speckle should be known in advance or pre-estimated [31] and then taken into account in filter and/or its parameters’ selection.
- Filter performance can be assessed using different quantitative criteria; for SAR image denoising, it is common to use peak signal-to-noise ratio (PSNR) and an efficient number of looks [11,17,18,19,20], although other criteria are applicable as well. In particular, it has become popular to use visual quality metrics [32,33,34]. Despeckling methods can be also characterized from the viewpoint of the efficiency of image classification after processing [35,36,37,38]. The SSIM metric [39] has become popular in remote sensing applications but this metric is clearly not the best visual quality metric [40,41,42] among those designed due to the current moment.
2. Image/Noise Model and Filter Efficiency Criteria
+ PSNR-HVS-M9 + PSNR-HVS-M11)/4,
3. Filtering Efficiency Prediction Using Trained Neural Network
3.1. Proposed Approach
3.2. Neural Network Input Parameters
4. NN Training Results
5. Adaptive Selection of Window Size
- To perform prediction for only one parameter, e.g., IPSNR, for all possible scanning window sizes, and to choose the window size for which the predicted metric (e.g., IPSNR) is the largest.
- To jointly analyze two or three metrics (e.g., IPSNR and IPHVSM or IPSNR, IPHVSM, and IFSIM) and undertake a decision (there probably are many algorithms to do this).
- To obtain three decisions based on the separate analysis of IPSNR, IPHVSM, and IFSIM as in the first item and then to apply the majority vote algorithm of some other decision rule.
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Predicted Metric | Scanning Window Size | RMSE | |
---|---|---|---|
IPSNR | 5 × 5 | 0.234 | 0.976 |
IPSNR | 7 × 7 | 0.289 | 0.986 |
IPSNR | 9 × 9 | 0.319 | 0.989 |
IPSNR | 11 × 11 | 0.355 | 0.990 |
IPHVSM | 5 × 5 | 0.208 | 0.966 |
IPHVSM | 7 × 7 | 0.303 | 0.983 |
IPHVSM | 9 × 9 | 0.351 | 0.988 |
IPHVSM | 11 × 11 | 0.396 | 0.989 |
IFSIM | 5 × 5 | 0.007 | 0.984 |
IFSIM | 7 × 7 | 0.011 | 0.987 |
IFSIM | 9 × 9 | 0.016 | 0.986 |
IFSIM | 11 × 11 | 0.019 | 0.985 |
Predicted Metric | Scanning Window Size | RMSE | |
---|---|---|---|
IPSNR | 5 × 5 | 0.263 | 0.966 |
IPSNR | 7 × 7 | 0.328 | 0.98 |
IPSNR | 9 × 9 | 0.361 | 0.985 |
IPSNR | 11 × 11 | 0.399 | 0.986 |
IPHVSM | 5 × 5 | 0.229 | 0.951 |
IPHVSM | 7 × 7 | 0.338 | 0.975 |
IPHVSM | 9 × 9 | 0.396 | 0.983 |
IPHVSM | 11 × 11 | 0.446 | 0.985 |
IFSIM | 5 × 5 | 0.008 | 0.981 |
IFSIM | 7 × 7 | 0.013 | 0.983 |
IFSIM | 9 × 9 | 0.017 | 0.982 |
IFSIM | 11 × 11 | 0.021 | 0.980 |
Predicted Metric | Scanning Window Size | RMSE | |
---|---|---|---|
IPSNR | 5 × 5 | 0.326 | 0.946 |
IPSNR | 7 × 7 | 0.412 | 0.967 |
IPSNR | 9 × 9 | 0.466 | 0.974 |
IPSNR | 11 × 11 | 0.512 | 0.977 |
IPHVSM | 5 × 5 | 0.279 | 0.929 |
IPHVSM | 7 × 7 | 0.431 | 0.961 |
IPHVSM | 9 × 9 | 0.513 | 0.971 |
IPHVSM | 11 × 11 | 0.573 | 0.974 |
IFSIM | 5 × 5 | 0.01 | 0.969 |
IFSIM | 7 × 7 | 0.016 | 0.973 |
IFSIM | 9 × 9 | 0.023 | 0.969 |
IFSIM | 11 × 11 | 0.028 | 0.964 |
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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://doi.org/10.3390/rs13101887
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://doi.org/10.3390/rs13101887
Chicago/Turabian StyleRubel, 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://doi.org/10.3390/rs13101887