FLE

Description

FLE applies a Lee adaptive filter on image data. The Lee filter is primarily used on SAR data to remove high-frequency noise (speckle) while preserving high-frequency features (edges).

Parameters

Name	Type	Length	Value range
Input: Input detected SAR image *	Raster port	1 -
Mask: Area mask	Bitmap port	0 - 1
Output: Output filtered image *	Raster port	1 - 1
Filter Size	Integer	0 - 1	3 | 5 | 7 | 9 | 11 | 13 | 15 | 17 | 19 | 21 | 23 | 25 | 27 | 29 | 31 | 33 Default: 7
Number of Looks	Float	0 - 1	1 - 100 Default: 1
Image Units	String	0 - 1	Amplitude | Power Default: Amplitude

* Required parameter

Parameter descriptions

Input: Input detected SAR image

Specifies the image layers to be filtered.

Mask: Area mask

Optionally specifies the bitmap that defines the area to be processed within the input raster. If this parameter is not specified, the entire layer is used by default. For a bitmap mask, you must specify the bitmap segment that you want to use. All of the pixels within the specified segment, having a pixel value of 1, define the area to be processed.

Only the area under the mask is written to the output.

Output: Output filtered image

Specifies the output channel(s) to receive the filtered results. If the specified output file already exists, the filtered channels will be appended to the existing file. If the output file does not already exist, a new file is created.

Filter Size

Optionally specifies the filter size of the square window, in pixel units. The value must be an odd integer between 3 and 33. The default size is 7 by 7 pixels. Only one value should be specified.

Number of Looks

Optionally specifies the effective number of looks of the SAR image. This is used to derive noise variance. Over homogeneous areas, the effective number of looks can be computed as the mean value squared divided by the variance (for amplitude data) or the mean divided by the standard deviation (for power data). By adjusting this parameter value, you can control the amount of smoothing applied to the image.

Acceptable values are 1 to 100. The default value of 1 is appropriate for single-look data and specifies maximum smoothing correction to the image.

Image Units

Optionally specifies the image format of the SAR image, which defines how the correction factor should be computed. By default, the image is assumed to be in amplitude units.

Image units can be "Amplitude" (or equivalently "AMP") or "Power" (or equivalently "POW").

Details

FLE applies Lee filtering on image data. The Lee filter smoothes out noise while retaining edges and sharp features in the image.

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

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

Algorithm

FLE performs spatial filtering on each individual pixel in an image using the gray-level values in a square window surrounding each pixel. The dimensions of the filter must be odd, and must be at least 3x3, with a maximum size of 11x11. The default filter size is 7x7.

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

        +----------+
        | a1 a2 a3 |
        | a4 a5 a6 |     <---  Filter window 3 X 3
        | a7 a8 a9 |
        +----------+
      

The Lee Filter function FLE is implemented as follows, for different types of noise.

Algorithm for additive noise

The resulting gray-level value R for the smoothed pixel is:

        R = I + K * (CP - I)
      

where:

        K = QVAR  / (QVAR + AVAR)
      

Algorithm for multiplicative noise

The resulting gray-level value R for the smoothed pixel is:

        R = I + K * (CP - U*I)
      

where:

        (MVAR/U**2)
        K = 1 - -----------
        (QVAR/I**2)
      

Algorithm for combined additive and multiplicative noise

The resulting gray-level value R for the smoothed pixel is:

        R = I + K * (CP - U*I - W)
      

where:

        K = (U*QVAR)  / (QVAR*U**2 + I**2*MVAR+AVAR)
      

The multiplicative noise variance is calculated from local statistics in the filter window:

        MVAR = (SD / I)**2
      

The mean value of additive noise is usually 0. The mean value of multiplicative noise is usually 1.

The THRVAR parameter (only for 7x7 and 9x9 windows) reduces noise in the edge areas, using the Improved Lee Filter. The filter redefines the specified filter window near the high-contrast regions, taking into account the orientation of edges. For each high local variance (high-contrast point) over the threshold (THRVAR), a gradient is computed to obtain the orientation of the edge. The subset of pixels in the local area on either side of the edge is then defined; the local variance is reduced and the noise along the edge is removed.

For further information on the Improved Lee Filter, refer to the following paper:

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

References

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

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.