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Purpose

 

Multi channel Intensity 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 Gaussian MAP

It is suitable for multi-channel Intensity dataset. The speckle is statistically modelled at the first order by a multivariate (real) Gaussian distribution. It is characterized by good performance in case of repeat-pass, tandem, and in general interferometric SAR datasets. Nevertheless, in case of mixed textures or very strong relief, it highlights its limits.

 

The input data must be previously coregistered.

 

Input Files

 

Input File List

Input file names of Intensity images (_pwr, _rsp). These files are mandatory.

 

Parameters - Principal Parameters

 

Window Size

Filter window size. 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 3/4-looks satellite images this distance rarely exceeds more than 4 pixels in both azimuth and range, i.e. it is useless to use a window size larger than 9 x 9 pixels.

 

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.

 

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 files

Output file names of filtered Intensity data. These files are mandatory.

 

_fil        

Filtered data and associated header files (.sml, .hdr).

 

_meta        

This file allows to load the specific processing results.

 

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.

 

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: ''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.