FLAP

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(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.

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").

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.

Report

Specifies where to direct the generated report.

Available options are:

• LOG: generates a report on the terminal (default)
• <modulename.RPT>: appends a report to the module's RPT file
• DISK: generates a report on file "IMPRPT.LST"

Details

FLAP applies Laplacian filtering on any type of image data. It is used primarily to filter speckled SAR data. The Laplacian filter smoothes out noise while retaining edges and sharp features in the image.

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.

The Laplacian filter model requires that the signal represent power. If the input image is in amplitude format, each gray level will be squared to derive power, and its square root will be applied to the filtered result.

Algorithm

The Laplacian filters are high-pass filters that act as local edge detectors. A characteristic of the Laplacian filter is that it is zero at points where the gradient is a maximum or a minimum. Consequently, points detected as gradient edges would generally not be detected as edge points with the Laplacian filter. Another characteristic of Laplacian operators is that a single gray level transition may produce two distinct peaks, one positive and one negative, which may be offset from the gradient location.

The Laplacian filter detects edges, regardless of direction. It produces sharper edges than most other edge detection filters.

L(f(x,y)) = (d**2)f / d(x**2) + (d**2)f / d(y**2)

(d**2)f / d(x**2) = f(x+1) - 2f(x) + f(x-1)

(d**2)f / d(y**2) = f(y+1) - 2f(y) + f (y-1)

L(f(x,y)) = f(x+1,y) + f(x-1,y) + f(x,y+1) + f(x,y-1) - 4f(x,y)

References

C.A. Lindley, 1991. "Area Processes", in Practical Image Processing in C. New York, John Wiley & Sons, Inc. pp. 374-5.

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.

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.

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