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<< Click to Display Table of Contents >> Basic Module - Filtering - Adaptive Non-Local SAR Filtering |
  
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<< Click to Display Table of Contents >> Basic Module - Filtering - Adaptive Non-Local SAR Filtering |
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Purpose
Images obtained from coherent sensors such as SAR systems are characterized by speckle. This is a spatially random multiplicative noise due to coherent superposition of multiple backscattering sources within a SAR resolution element. In other words, speckle is a statistical fluctuation associated with the radar reflectivity of each pixel in a scene. A first contribution to reduce the speckle - at the expense of spatial resolution - is usually performed during the multi-looking step, where range and/or azimuth resolution cells are averaged.
In order to reduce the speckle noise, a non-local filter, specifically designed for SAR non-additive noise, has been developed. This tool is based on the paper reported in the References paragraph with the addition of a custom locally-adaptive similarity probability kernel. This kernel is used to overcome local distortions caused by strong backscattering variations, typical of the original filter design.
The number of iterations for the weighted denoising is set to 3 by default.
Non-local-based filtering is an intrinsically, computationally demanding feature, due to its many comparisons between different image areas. As a consequence, this tool put the underlying hardware components under stress and requires a significant amount of time. For this reason, our implementation is entirely written in OpenCL to take advantage of the massive level of parallelization offered by modern graphical processing units. Hence, we warmly recommended to use this filter only in combination with a powerful GPU selected as the current OpenCL platform/device under the SARscape Common Preferences. It is always possible to select and rely to a CPU-only OpenCL platform/device, but in such a case performance will roughly be one order of magnitude lower than using a modern GPU.
Technical Note
This tool has been tested and validated using slant-range images, for which the assumptions on the multiplicative noise probability density function are the ones reported in the referenced paper.
For what concerns the Estimated Number of Looks (ENL) parameter, it is suggested to leave it as default (i.e., to -1) for single-look data, while in case of multi-looked data the ENL could be manually set by estimating it over a homogeneous area of the image.
The T and H parameters act as weights to balance the trade-off between the noise reduction and the fidelity of the estimate.
Input Files
Input file list
Input file names (_pwr, _rsp). This file list is mandatory.
Parameters - Principal Parameters
Equivalent Number of Looks (ENL)
The Equivalent Number of Looks is equivalent to the number of independent intensity values averaged per pixel during the multi-looking process. This parameter can be easily estimated over a homogeneous (stationary) sample in the input intensity data according to:
ENL = mean2 / standard deviation2
The higher ENL value, the lower the filtering power (H factor and T factor are reduced) in order to avoid over-smoothing.
Non-Local H factor
It modules the filter intensity. The higher the value, the higher the strength of the filter.
Non-Local T factor
It modules the filter intensity during the iterative processing. The higher the value, the higher the strength of the filter.
Non-Local Average Window Size
The size of the weighted average performed by the filter. By increasing the window size, the waving effect over homogeneous areas is reduced. However, the processing time exponentially increases in function of the window size.
Non-Local Similarity Kernel Size
The kernel size used to quantify the similarity probability (weight) for the weighted average.
Non-Local Similarity Min Kernel Size
This parameter activates the similarity probability kernel adaptivity (if this value is lower than the similarity kernel size).
Parameters - Global
It brings to the general section of the Preferences parameters. Any modified value will be used and stored for further processing sessions.
Parameters - Other Parameters
It brings to the general section of the Preferences parameters. Any modified value will be used and stored for further processing sessions.
Output Files
Output file list
Output file list of the filtered data. This file list is mandatory.
_fil
Filtered intensity image and associated header files (.sml, .hdr).
Details specific to the Units of Measure and Nomenclature of the output products can be found in the Data Format section.
General Functions
Exec
The processing step is executed.
Store Batch
The processing step is stored in the batch list. The Batch Browser button allows to load the batch processing list.
Close
The window will be closed.
Help
Specific help document section.
Specific Function(s)
None.
Task, SARscapeBatch object, SARscapeBatch script example
References Deledalle, C. A., Denis, L., & Tupin, F. (2009). Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Transactions on Image Processing, 18(12), 2661-2672.