| Environments | PYTHON :: EASI :: MODELER |
| Batch Mode | Yes |
| Quick links | Description :: Parameters :: Parameter descriptions :: Details :: Algorithm :: Acknowledgements :: Related |
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| Name | Type | Length | Value range |
|---|---|---|---|
| InputRef: Reference image * | Raster port | 1 - 1 | |
| InputDep: Dependant image * | Raster port | 1 - 1 | |
| Input: Images to coregister | Raster port | 0 - 1 | |
| MODELTYP | String | 0 - | |
| Number of Candidate GCPs | Integer | 0 - 1 | 1 - Default: 500 |
| Minimum correlation score | Float | 0 - 1 | 0.72 - 1 Default: 0.75 |
| Search radius (in pixels) | Integer | 0 - 1 | |
| Report | String | 0 - 192 | See parameter description |
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InputRef: Reference image
The name of the file to use as the reference data set.
InputDep: Dependant image
The name of the file to use as the dependant data set.
Input: Images to coregister
The name of the file to resample and coregister.
MODELTYP
The type of model to fit to the collected GCPs, the model is then used to interpolate pixel shifts between GCPs. The valid options are:Number of Candidate GCPs
The number of grid points to distribute over the reference image. The corresponding location, based on geocoding information, is used as the center of the search radius in the dependent file.The value must be greater than or equal to 1. The default value is 500.
Minimum correlation score
The minimum acceptable correlation score. Match points with a lower correlation score will be rejected. The value must be in the range of 0 (no correlation) to 1 (perfect correlation). The default value is 0.75, and is the recommended minimum value.Search radius (in pixels)
The search radius, in pixels, to examine to find a match between the reference and dependent images. The value must be greater than or equal to 1. The default value is 50.Report
Specifies where to direct the generated report.
Available options are:
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The reference channel and the dependent channel you specify are used to generate image match points. The number of candidate points you specify are distributed evenly (in a grid) over the reference image. With each candidate reference point, a corresponding location in the dependent file is determined. The area surrounding the dependent location is scanned to find the image match with the highest score.
Dependent points with a correlation below the threshold you specify are removed. The remaining match points are used to derive the mapping of the low-order-polynomial, which is applied to all channels specified in the input channel list. The resampled complex-valued output contains the number of channels, and the order of occurrence, corresponding to the input channel list.
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The value of the output pixel is determined by using a two-dimensional sinc function; that is, sin (x)/x), as the resampling kernel over a 16-by-16 neighborhood.
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