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Purpose

 

Single Look Complex data can be filtered using any of the following procedures:

 

 

Optimal results can be achieved by selecting the appropriate filter depending on the land morphology and scene texture, this last to be considered also in relationship with the data spatial resolution.

 

Technical Note

 

Gaussian Gamma MAP

This filter is suitable for a single acquisition. If the user provides many images the tool independently filters each image. It is suitable in case of images characterized by regular mixed texture or moderate relief.

 

Input Files

 

Input File List

Input file names of Single Look Complex (_slc) data. These files are mandatory.

 

Parameters - Principal Parameters

 

Window Size in Azimuth and Range

Filter window size (square or rectangular). The signal samples to be used for the computation of the local statistics are collected within a neighborhood where the autocorrelation function of the radar reflectivity is significant, in order to avoid taking samples within areas of a different nature than the pixel under process. In practice, this means that signal samples will not be collected farther than a certain distance - the decorrelation length - from the pixel under process. For SLC data, this distance rarely exceeds more than 12 pixels in azimuth and 4 pixels in range, i.e. it is useless to use a window size larger than 31 x 9 (azimuth/range) pixels. In theory a processing window size of 25 x 9 (azimuth/range) pixels is sufficient; in practice, with new sensors, a window size of 7 x 7 is sufficient.

 

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

 

In case that ENL is not set, the software tries to retrieve it automatically; if it fails it takes the Number of Looks (NL) used during the multi-looking process is considered.

 

Note that, to tune the strength of speckle filtering and the level of preservation of scene details, it is preferable to adjust the value of the ENL, rather than to change the size of the processing window:

 

-To reduce the strength of speckle filtering, with the aim to preserve the thinnest details of the scene, enter a ENL value slightly higher than the calculated one;

-Inversely, to improve the filtering of the speckle (possibly at the cost of the thinnest details of the scene), enter a ENL value slightly lower than the calculated one.

 

Azimuth Looks

The azimuth multi-looking factor can be entered only if Gaussian Gamma MAP or Gaussian DE-MAP filters are selected.

 

Range Looks

The range multi-looking factor can be entered only if Gaussian Gamma MAP or Gaussian DE-MAP filters are selected.

 

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 names of the filtered (possibly multi-looked) Intensity data. These files are mandatory.

 

_fil        

Filtered data 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.        

 

See Also

 

Task, SARscapeBatch object, SARscapeBatch script example

 

References

 

Lopes A., E. Nezry, R. Touzi, and H. Laur: "Structure detection and statistical adaptive speckle filtering in SAR images". International Journal of Remote Sensing, Vol. 14, 1993.

 

E. Nezry, M. Leysen, and G. De Grandi: "Speckle and scene spatial statistical estimators for SAR image filtering and texture analysis: some applications to agriculture, forestry, and point targets detection". Proceedings of SPE, Vol. 2584, 1995.

 

Lopes A., J. Bruniquel, F. Sery, and E. Nezry: "Optimal Bayesian texture estimation for speckle filtering of detected and polarimetric data". Proceedings of IGARSS’97 Symposium, 1997.

 

Nezry E., A. Lopes, and F. Yakam-Simen. "Prior scene knowledge for the Bayesian restoration of mono and multi-channel SAR images". Proceedings of IGARSS’97 Symposium, 1997.

 

Nezry E. and F. Yakam Simen: "Control systems principles applied to speckle filtering and geophysical information extraction ion multi-channel SAR images". Proceedings of IGARSS’97 Symposium, 1997.

 

Nezry E., F. Zagolsky, F. Yakam-Simen, and I. Supit: "Control systems principles applied to speckle filtering and to the retrieval of soil physical parameters through ERS and Radarsat-1 SAR data fusion", 1998.

 

E. Nezry and F. Yakam Simen: "New distribution-entropy Maximum A Posteriori speckle filters for detected, complex, and polarimetric SAR data". Proceedings of IGARSS'99 Symposium, 1999.

 

Nezry E. and F. Yakam Simen: "A family of distribution-entropy MAP speckle filters for polarimetric SAR data, and for single or multi-channel detected and complex SAR images". Proceedings of the CEOS SAR Workshop, ESA SP-450, 1999.

 

E. Nezry: ''Adaptive Speckle Filtering in Radar Imagery'' from the edited volume Land Applications of Radar Remote Sensin Edited by F. Holecz, P. Pasquali, N. Milisavljevic and D. Closson, 2014.