| Environments | PYTHON :: EASI :: MODELER |
| Batch Mode | Yes |
| Quick links | Description :: Parameters :: Parameter descriptions :: Details :: Related |
| Back to top |
| Back to top |
| Name | Type | Length | Value range |
|---|---|---|---|
| InputRef: Input reference image channels * | Raster port | 1 - 1 | |
| InputORG: Input original image channel * | Raster port | 1 - 1 | |
| OutputB0: Output channel for B0 * | Raster port | 1 - 1 | |
| OutputB1: Output channel for B1 * | Raster port | 1 - 1 | |
| Maximum Model Gain | Float | 0 - 1 | 0.0 - 255.0 Default: 3.0 |
| Kernel Size | Integer | 0 - 1 | 1 - 100 Default: 7 |
| Report | String | 0 - 192 | See parameter description |
| Back to top |
InputRef: Input reference image channels
Specifies the image channel to be used as the image reference in the algorithm. This should be an 8-bit unsigned image channel.
InputORG: Input original image channel
Specifies the input channel that contains the original image. This should be an 8-bit unsigned image channel.
OutputB0: Output channel for B0
Specifies the image channel to receive the output B0 image. This should be a 32-bit real image channel.
OutputB1: Output channel for B1
Specifies the image channel to receive the output B1 image. This should be a 32-bit real image channel.
Maximum Model Gain
Specifies the maximum model gain to be used for the modeling process.
Lower maximum gain values reduce the tendency of the algorithm to introduce noise into otherwise low texture or structured areas such as water regions. A typical starting value for this parameter is 3. If there is resulting noise in the water regions, reduce the maximum gain value. Note that reducing this value to less than 1 will result in blurred edges.
Kernel Size
Specifies the size of the linear kernels over which the cross-correlation modeling is performed.
Correlation is performed on a window size of ( 2 * KSIZE + 1 ) pixels. Sizes of less than 3 will produce noisier images; sizes greater than 7 will blur edges.
Report
Specifies where to direct the generated report.
Available options are:
| Back to top |
CROSSMOD performs a cross correlation between the reference image and the original image. A horizontal and vertical kernel of KSIZE length is created. As this kernel scans across the window, the merit of both the horizontal and vertical kernels is evaluated. The correlation coefficients (B0 and B1) of the kernel with the best merit are then saved to the output file.
When spatial enhancement is performed, the amount of detail from the reference image that will be fused into the output image can be controlled by specifying the maximum gain (MAXGAIN); this parameter sets the upper limit of the B1 coefficient of the kernel with the best merit. For example, setting the maximum gain to 0 produces the original input file as output; there will be no spatial enhancement.
© PCI Geomatics Enterprises, Inc.®, 2026. All rights reserved.