FEFROST

Description

Parameters

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 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 X Size

Optionally specifies the horizontal size of the filter, in pixel units. This value must be an odd integer between 1 and 33. The default value is 7 pixels.

Filter Y Size

Optionally specifies the vertical size of the filter, in pixel units. This value must be an odd integer between 1 and 33. The default value is 7 pixels.

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.

Instead of 1, the default value of this parameter will be the file level metadata value for 'NumLooks' if this metadata exists.

Damping Factor

Optionally specifies the damping constant for the adaptive filter. This constant specifies the extent of the damping effect of the filtering. The default value of 1.0 is sufficient for most SAR images.

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.

Details

FEFROST is used primarily to filter speckled SAR data. The filter smoothes out noise while retaining edges and sharp features in the image. The algorithm consists of an adaptive filter where the result is further smoothed by the damping factor.

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.

The Enhanced Frost filter divides an image into areas of three classes. The first class corresponds to the homogeneous areas in which the speckles may be eliminated by applying a low-pass filter (or averaging, multi-look processing). The second class corresponds to the heterogeneous areas in which the speckles are to be reduced while preserving texture. The third class contains areas representing isolated point targets, for which the filter should preserve the observed value.

Algorithm

The filter output is:

        R = I         if (Ci < Cu)
        R = Rf      if (Cu<=Ci<=Cmax)
        R = CPIXEL      if (Ci > Cmax)
      

      M = exp(-DAMP*(Ci-Cu)/(Cmax-Ci) * T)

        Ci  = sqrt(V)/I
        Cu  = 1/sqrt(NLOOK)
      Cmax = sqrt(1+2/NLOOK)

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

        Rf = (P1*M1 + P2*M2 + ... +  Pn*Mn) / (M1 + M2 + ... + Mn)
      

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

For amplitude images, each gray level is squared, and its square root applied to the final result.

The following paper provides a detailed comparison of different radar filtering methods:

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

Acknowledgements

PCI Geomatics wishes to acknowledge the assistance of Ko B. Fung and Zhenghao Shi at Canada Centre for Remote Sensing for providing source code and assistance of their programs. Special thanks to Dr. R. Touzi from Canada Centre for Remote Sensing for his helpful suggestions and comments.

References

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.