See Also: FSPEC, FAV, FED, FGA, FME, FMO, FSHARP, FSOBEL, FPR, FPRE
| Type: | Raster |
| Connection: | Mandatory |
| Minimum Layers: | 1 |
| Maximum Layers: | 1024 |
Contains the data to be filtered.
| Type: | Bitmap |
| Connection: | Optional |
| Minimum Layers: | 0 |
| Maximum Layers: | 1 |
Specifies the bitmap layer under which the input raster should be processed. If no bitmap is provided the entire window is processed.
| Type: | Raster |
| Connection: | Optional |
| Minimum Layers: | 1 |
| Maximum Layers: | 1024 |
Contains the filtered data.
| Name: | FiltPixels |
| Type: | Integer |
| Valid Values: | 1,3,5,....31 |
| Default: | 3 |
| Requirement: | Mandatory |
Specifies the filter x size.
| Name: | FiltLines |
| Type: | Integer |
| Valid Values: | 3,5,7,....31 |
| Default: | 3 |
| Requirement: | Mandatory |
Specifies the filter y size.
| Name: | NumberLooks |
| Type: | Real |
| Valid Values: | 0.0 <= x <= 100.0 |
| Default: | 1.0 |
| 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.
| 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.
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. FSTDDEV 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 channel 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 Standard Deviation 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.
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.