FLE -- Lee SAR Speckle Filter

Applies a Lee speckle filter on a SAR image. This filter is primarily used on radar data to remove high frequency noise (speckle), while preserving high frequency features (edges).

See Also: FSPEC, FAV, FED, FGA, FME, FMO, FSHARP, FSOBEL, FPR, FPRE

INPUT PORT(S)

Input

Type:Raster
Connection:Mandatory
Minimum Layers:1
Maximum Layers:1024

Contains the input image layer(s) consisting of the data to be filtered.

Mask

Type:Bitmap
Connection:Optional
Minimum Layers:0
Maximum Layers:1

Contains the input Area Mask layer, that is, a bitmap indicating the area under which the input raster should be processed. If no bitmap is provided the entire layer is processed.

OUTPUT PORT(S)

Output

Type:Raster
Connection:Optional
Minimum Layers:0
Maximum Layers:1024

Contains the output layer(s) that will receive the filtered image(s).

INPUT PARAMETER(S)

FLE is controlled by the following parameters:

Image Type

Name:ImageType
Type:Text
Valid Values:AMP, POW
Default:AMP
Requirement:Mandatory

Specifies the image format of the radar image, which can be supplied in either the amplitude or power (intensity) format.

Amplitude is the square root of power. Most radar images are supplied in the amplitude format to preserve the values.

Filter X Size (Pixels)

Name:FiltPixels
Type:Integer
Valid Values:3, 5, 7, 9, 11
Default:3
Requirement:Mandatory

Specifies the filter's x size, in units of pixels.

Filter Y Size (Lines)

Name:FiltLines
Type:Integer
Valid Values:3, 5, 7, 9, 11
Default:3
Requirement:Mandatory

Specifies the filter's y size, in units of lines.

Number of Looks

Name:NumberLooks
Type:Integer
Valid Values:0 <= x <= 100
Default:1
Requirement:Mandatory

Specifies the (effective) number of looks of the image. This is used to derive noise variance. By adjusting NumberLooks, the user can control the amount of smoothing applied to the image.

DETAILS

The Lee filter is used primarily to filter speckled radar data. It is designed to smooth out noise while retaining edges or shape features in the image.

The filter size can be specified through the FilterSize parameters. Different filter sizes will greatly affect the quality of processed images. If the filter is too small, the noise filtering algorithm is not effective. If the filter is too large, subtle details of the image will be lost in the filtering process. A 7x7 filter usually gives the best results.

The NumberLooks parameter is used to estimate noise variance and it effectively controls the amount of smoothing applied to the image by the filter. Theoretically, the correct value for NumberLooks should be the effective number of looks of the radar image. It should be close to the actual number of looks, but may be different if the image has undergone resampling. The user may experimentally adjust the NumberLooks value so as to control the effect of the filter. A smaller NumberLooks value leads to more smoothing; a larger NumberLooks value preserves more image features.

FLE performs spatial filtering on each individual pixel in an image using the grey level values in a square window surrounding each pixel. The dimensions of the filter must be odd, and must be at least 3x3.

All pixels are filtered. In order to filter pixels located near the edges of the image, edge-pixel values are replicated to give sufficient data.

A bitmap specifies the area within the input layer which will be processed. Only this area will be filtered and the rest of the image will be unchanged. If no bitmap is connected, the entire database is processed.

The Lee filter model requires that the signal represents power. If the input image is in amplitude format, each grey level will be squared to derive power and finally square root will be applied to the filtered result.

ALGORITHM

Implementation of the speckle filters are based on the following papers, and especially the review paper by Shi and Fung. 

 Jong-Sen Lee, "Digital Image Enhancement and Noise Filtering
 by Use of Local Statistics", IEEE Transactions on Pattern
 Analysis and Machine Intelligence, Vol. PAM1-2, No. 2, March, 1980.

 J.S.Lee, "Refined Filtering of Image Noise Using Local Statistics"
 Computer Graphic and Image Processing 15, 380-389 (1981)

 D.T. Kuan, A.A. Sawchuk, T.C. Strand, and P. Chavel, 
 "Adaptive restoration of images with speckle," IEEE Trans. ASSP.,
 Vol. 35, no. 3, pp. 373-383, March 1987.

 A. Lopes, R. Touzi and E. Nezry, "Adaptive speckle filters and
 Scene heterogeneity", IEEE Transaction on Geoscience and Remote
 Sensing, Vol. 28, No. 6, pp. 992-1000, Nov. 1990.

 V.S. Frost, J.A. Stiles, K.S. Shanmugan, and J.C. Holtzman,
 "A model for radar images and its application to adaptive digital 
 filtering of multiplicative noise," IEEE Trans. Pattern Analysis
 and Machine Intelligence, vol. 4, no. 2, pp. 157-166, March 1982. 

 A. Lopes, E. Nezry, R. Touzi, and H. Laur, "Structure detection
 and statistical adaptive speckle filtering in SAR images",
 International Journal of Remote Sensing, Vol. 14, No. 9, 
 pp. 1735-1758, 1993.

 A. Lopes, R. Touzi and E. Nezry, "Adaptive speckle filters and
 Scene heterogeneity", IEEE Transaction on Geoscience and Remote
 Sensing, Vol. 28, No. 6, pp. 992-1000, Nov. 1990.

 Zhenghao Shi and Ko B. Fung, 1994, "A Comparison of Digital
 Speckle Filters", Proceedings of IGRASS 94, August 8-12, 1994.

LEE FILTER

Speckle noise in SAR images is generally assumed a multiplicative error model. In the Lee filter, the multiplicative model is first approximated by a linear model. Then the minimum mean square error criterion is applied to the linear model.

The resulting grey level value R for the smoothed pixel is: 

       R = Ic * W + Im * (1 - W)
where: 

       W   =   1 - Cu^2/Ci^2
       Cu  =   SQRT(1/NLOOK)
       Ci  =   S / Im
       Ic  =   center pixel pixel of filter window
       Im  =   mean value of intensity within window
       S   =   standard deviation of intensity within window
The Cu above is the estimated noise variation coefficient. Ci is the image variation coefficient. W is a weighting function.

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