FLAP

Description

Parameters

Parameter descriptions

FILI

Specifies the name of the file that contains the image data to be filtered and to receive the output.

FILO

Specifies the output channel to receive the filtered results. This channel must already exist within the file specified by FILO. If this parameter specifies the same channel as the specified input channel (DBIC), DBOC overwrites the input channel.

DBIC

Specifies the channel in the input detected SAR image to be processed.

MASK

Optionally specifies the window or bitmap that defines the area to be processed within the input raster. If this parameter is not specified, the entire channel is processed.

A window mask is specified as follows:

MASK=Xoffset, Yoffset, Xsize, Ysize 

Xoffset, Yoffset define the upper-left starting pixel coordinates of the window. Xsize is the number of pixels that define the window width. Ysize is the number of lines that define the window height.

For a bitmap mask, you can specify the bitmap segment number from the input file (FILI) that you want to use. All 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.

DBOC

Specifies the output channel to receive the filtered results. This channel must already exist within the input file (FILI). If this parameter specifies the same channel as the one defined as the input (DBIC), DBOC overwrites the input channel.

IMAGEFMT

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").

FLSZ

Optionally specifies the horizontal and vertical dimensions of the filter, in pixel units. Acceptable values are odd integers from 1 to 33, inclusively. The filter need not be square. Minimum filter sizes are 1x3 or 3x1; the default size is 7x7.

REPORT

Specifies where to direct the generated report.

Available options are:

• TERM: generates a report on the terminal (default)
• DISK: generates a report on file "IMPRPT.LST"
• OFF: turns off report generation
• <filename>: appends a report to the specified file

MONITOR

The program progress can be monitored by printing the percentage of processing completed. A system parameter, MONITOR, controls this activity.

Available options are:

• ON: Turns the monitor ON (default)
• OFF: Turns the monitor OFF (recommended if running in batch or background mode)

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.

Example

Run a Laplacian filter on channel 1 of "irvine.pix".

EASI>FILI	=	"irvine.pix"
EASI>FILO	=	FILI    ! Output into input file
EASI>DBIC	=	1
EASI>MASK	=	11      ! Processes only the area under the bitmap stored in segment 11
EASI>DBOC	=	8	! output channel
EASI>IMAGEFMT	=		! defaults to Amplitude
EASI>FLSZ	=	7,7	! 7x7 filter

EASI>run FLAP

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).