RasterRegisterer

Transforms an image to minimize its difference with another.

Typical Uses

Medical image processing
Image noise reduction
Computer Vision

How does it work?

The RasterRegisterer accepts two sets of raster features:

Fixed Images, through the Fixed port, which provide a reference onto which moving images will be transformed.
Moving Images, through the Moving port, which will be transformed to match the fixed one.

Fixed images and moving images will be grouped according to the Group By parameter. For each pair of matched features, a new feature will be output from the Registered output port. This feature will be the registered version of the moving image.

Non-raster features will be rejected.

The registration process

The registration process operates on two images. Once a moving image is paired with a fixed image, the registration process begins.

The moving image is transformed in the attempt to minimize the “difference” with the fixed image, which is determined by a metric. A metric is a function that takes to images and outputs a number that indicates how different they are. The metric value increases when the fixed and moving images are increasingly dissimilar. This transformer attempts to construct a transformation that, when applied to the moving image (creating the registered image), minimizes the metric value.

The construction of such a transformation is treated as an optimization problem. The RasterRegisterer starts from an initial transformation and iteratively modifies it in an attempt to minimize the metric value. This process can stop when either a certain tolerance is met, no further improvements can be made, or after a certain number of iterations.

Transformation modifications are performed according to the specified optimizer. The optimizer is a tool that evaluates whether the last change improved the metric value and takes action accordingly, to make a new change to the transformation. At every iteration the change made to the metric is called the step. The way the optimizer controls the transformation is by its transformation parameters. Transformation parameters are a set of free variables that uniquely determine a transformation and are exposed to the optimizer.

The RasterRegisterer offers a choice of Metric, Transformation, and Optimizer options.

Selecting Raster Bands and Palette

The RasterRegisterer operates on whole rasters and ignores any specific band or palette selection.

Alpha Bands and Nodata values

Alpha bands can be used to determine whether a certain pixel of the image will be used for registration.

Both the moving and fixed image may have an alpha band. If, at a given point, either the moving or the fixed image’s alpha band have a zero value, that point will not be used for metric evaluation.

For all values other than zero, the metric will be evaluated at that point. What the value is makes no difference.

Rasters with more than one alpha band will be rejected.

Attributes

Attributes from the moving feature will be preserved when output via the Registered port. Rejected features will preserve attributes from both ports.

Coordinate system handling

Features must be in comparable coordinate systems to be registered.

If the Fixed Image of a group has specified a coordinate system then all the Moving Images must have a coordinate system as well. Moving features without a coordinate system would be rejected in this case.

Whereas, if the Fixed Image does not have coordinate system specified, moving features in the same group, that do specify a coordinate system, will be rejected.

If the coordinate systems of a Moving Image and Fixed Image differ, the RasterRegisterer will attempt to reproject the Moving Image onto the coordinate system of the Fixed Image before registering. Moving images that could not be reprojected will also be rejected.

The Registered feature will be in the coordinate system space of the Fixed image.

Usage Notes

Choosing a Raster Transformer

FME has an extensive selection of transformers for working with raster data. They can be generally categorized as working with whole rasters, bands, cells or palettes, and those designed for workflow control or combining raster with vector data.

For information on raster geometry and properties, see Rasters (IFMERaster).

Raster Transformers

Working with Rasters

These transformers generally apply to whole rasters.

RasterCellOriginSetter	Sets the cell origin point within cells in a raster.
RasterConvolver	Applies a convolution filter (sometimes called a kernel or lens) to raster features and outputs the results.
RasterExpressionEvaluator	Evaluates expressions on each cell in a raster or pair of rasters, including algebraic operations and conditional statements.
RasterExtentsCoercer	Replaces the geometry of input raster features with a polygon covering either the extents of a raster or the extent of data within a raster.
RasterGCPExtractor	Extracts Ground Control Point (GCP) coordinate system and point values from a raster feature and exposes them as attributes.
RasterGCPSetter	Sets Ground Control Points (GCPs) on a raster, pairing cell positions with known coordinates.
RasterGeoreferencer	Georeferences a raster by either known corner coordinates or origin, cell size, and rotation.
RasterHillshader	Generates a grayscale shaded relief representation of terrain, based on elevation values.
RasterInterpretationCoercer	Alters the interpretation type of rasters, including all bands, and converts cell values if necessary.
RasterMosaicker	Merges multiple raster features into a single raster feature.
RasterPropertyExtractor	Extracts the geometry properties of a raster feature and exposes them as attributes.
RasterPyramider	Resamples rasters to multiple resolutions, based on either number of levels or dimensions of the smallest output raster.
RasterRegisterer	Transforms an image to minimize its difference with another.
RasterResampler	Resamples rasters, based on specified output dimensions, cell size in ground units, or percentage of original, and interpolates new cell values.
RasterRotationApplier	Rotates a raster feature according to its rotation angle property, interpolating new cell values, updating all other affected raster properties, and producing an output raster feature with a rotation angle of zero.
RasterSharpener	Enhances the features of a raster image. The RasterSharpener enhances the borders, lines, and curves while reducing noise in the flat areas of the raster image.
RasterSubsetter	Clips raster features using pixel bounds instead of ground coordinates, and optionally adds cells around the perimeter.
RasterTiler	Splits each input raster into a series of tiles by specifying either a tile size in cells/pixels or the number of tiles.
RasterToPolygonCoercer	Creates polygons from input raster features. One polygon is output for each contiguous area of pixels with the same value in the input raster.
WebMapTiler	Creates a series of image tiles that can be utilized by web mapping applications such as Bing™ Maps, Google Maps™, or Web Map Tile Service. This is done by resampling rasters to various different resolutions and then splitting them into tiles.

Working with Bands

These transformers generally apply to bands.

RasterBandAdder	Adds a new band to a raster feature.
RasterBandCombiner	Merges coincidental raster features into a single output raster feature, preserving and appending all bands.
RasterBandInterpretationCoercer	Alters the interpretation type of individual raster bands, converting cell values if necessary.
RasterBandKeeper	Removes all unselected bands from a raster feature.
RasterBandMinMaxExtractor	Extracts the minimum and maximum band values, palette keys, and palette values from a raster feature, and adds them to a list attribute.
RasterBandNameSetter	Sets the band name of selected bands on a raster, making raster contents simpler to understand compared to band numbers.
RasterBandNodataRemover	Removes the existing Nodata identifier from selected bands of a raster feature. Any values previously equal to the Nodata value are considered valid data.
RasterBandNodataSetter	Sets a new Nodata value on selected bands of a raster feature.
RasterBandOrderer	Specifies the required order of bands in a raster. Bands are reordered according to the input band indices.
RasterBandPropertyExtractor	Extracts the band and palette properties of a raster feature and exposes them as attributes.
RasterBandRemover	Removes any selected bands from a raster feature.
RasterBandSeparator	Separates bands or unique band and palette combinations, and outputs either individual raster features or a single new raster feature containing all combinations.
RasterStatisticsCalculator	Calculates statistics on raster bands and adds the results as attributes.

Working with Cells

These transformers generally apply to individual cells.

RasterAspectCalculator	Calculates the aspect (direction of slope) for each cell of a raster. Aspect is measured in degrees from 0 to 360, clockwise from north.
RasterCellCoercer	Creates individual points or polygons for each cell in a raster, optionally extracting band values as z coordinates or attributes.
RasterCellValueCalculator	Evaluates basic arithmetic , minimum, maximum or average operations on the cell values of a pair of rasters.
RasterCellValueReplacer	Replaces a range of band values in a raster with a new single value.
RasterCellValueRounder	Rounds off raster cell values.
RasterSegmenter	Partitions a raster image into arbitrarily sized groups of cells from the input image based on intensity differences in the input raster image cells.
RasterSingularCellValueCalculator	Performs basic arithmetic operations on the cell values of a raster against a numeric value.
RasterSlopeCalculator	Calculates the slope (maximum rate of change in z) for each cell of a raster.

Working with Palettes

These transformers generally apply to palettes.

RasterPaletteAdder	Creates a palette from an attribute, and adds this palette to all selected bands on a raster.
RasterPaletteExtractor	Creates a string representation of an existing palette on a raster and saves it to an attribute.
RasterPaletteGenerator	Generates a palette out of the selected band(s) of a raster. The output raster will have the selected band(s) replaced by a new band with a palette.
RasterPaletteInterpretationCoercer	Alters the interpretation type of raster palettes.
RasterPaletteNodataSetter	Identifies the palette key that matches a raster band’s Nodata value, and sets a value on it.
RasterPaletteRemover	Removes selected palette(s) from raster features.
RasterPaletteResolver	Resolves the palette(s) on a raster by replacing cell values with their corresponding palette values. Palette values with multiple components, such as RGB, are broken down and the individual values assigned to multiple, newly-added bands.

Workflow Control

These transformers generally control the flow of features in a workspace.

RasterCheckpointer	Forces accumulated raster operations to be processed, saving the state to disk and releasing resources to tune performance or assist with memory limitations.
RasterConsumer	Reads raster features for testing purposes, including any accumulated raster operations. No additional operations are performed, and nothing is done with the features.
RasterExtractor	Serializes the geometry of a raster feature into a Blob attribute, encoding the contents according to a choice of common binary raster formats.
RasterNumericCreator	Creates a numeric raster of specified size and resolution, with default cell values.
RasterReplacer	Decodes a binary attribute containing encoded rasters stored as Blobs, replacing the feature’s geometry with the decoded raster.
RasterRGBCreator	Creates a color raster feature of specified size, resolution, and interpretation type, with default cell values.
RasterSelector	Selects specific bands and palettes of a raster for subsequent transformer operations.

Vectors and Rasters

These transformers generally involve using rasters and vector data together.

ImageRasterizer	Creates a raster representation of vector or point cloud input features, using the fme_color attribute over a solid background fill for vector features. Point clouds may be rendered using their color or intensity components.
NumericRasterizer	Creates a numeric raster representation of vector or point cloud input features, where cell values are taken from the z coordinates of the input features and overlaid on a uniform background.
MapnikRasterizer	Generates a raster from input vector and raster features, with fine control over symbolization and labeling, using the Mapnik toolkit.
PointCloudOnRasterComponentSetter	Sets point cloud component values by overlaying a point cloud on a raster. The component values for each point are interpolated from band values at the point location.
PointOnRasterValueExtractor	Extracts the band and palette values from a raster at the location of one or more input points and sets them as attributes on the feature.
RasterDEMGenerator	Produces a raster digital elevation model (DEM) by uniformly sampling a Delaunay triangulation generated from input points and breaklines.
VectorOnRasterOverlayer	Rasterizes vector or point cloud features onto an existing raster. For vector features the fme_color attribute sets pixel color, and point clouds may be rendered using their color or intensity components.

Configuration

Input Ports

Output Ports

Parameters

Group Processing

Group By

If Group By attributes are selected, features with the same values in the Group By attributes are grouped together, and features will only be registered with other features in the same group. All moving images of a given group of features will be registered against the single fixed image of that group.

Optional

Group By Mode

Process At End (Blocking): This is the default behavior. Processing will only occur in this transformer once all input is present.

Process When Group Changes (Advanced): This transformer will process input groups in order. Changes of the value of the Group By parameter on the input stream will trigger processing on the currently accumulating group. This may improve overall speed (particularly with multiple, equally-sized groups), but could cause undesired behavior if input groups are not truly ordered.

Registration Parameters

These determine what kind of registration you are performing.

Metric

The metric is the function that, given two images, returns a number indicating how much they differ one from the other.

Single Modality Default (Default): Alias to the Mean Squares metric.

Multi Modality Default: Alias to the Mattes Mutual Information metric.

Mean Squares: Use the Mean of the squared pixel values to compute how different the images are.

Useful for comparing images of the same modality (MRIs with MRIs and Visible images with Visible images) with similar intensities.

This metric is positive and smaller values indicate a better match.

Correlation: Normalized Cross Correlation image metric. Works well in deformable registration of images of the same modality, with similar intensities.

This metric is negative and negative values of greater magnitude indicate a better match.

Mattes Mutual Information: Mutual Information Metric by Mattes et al. This metric is designed to handle images of different modalities, but works well.

This metric is negative and negative values of greater magnitude indicate a better match.

Required

Transformation

Choose how the moving image can be transformed to match the moving one.

Affine: (Default) Transformation that preserve points and straight lines. (3 parameters)

Rigid: Transformation that preserves all shapes and areas. (6 parameters)

BSpline: Deformable transformation using a BSpline representation with third order splines. A deformation is defined by setting deformation vectors for each point on a sparse regular grid of control points, called mesh points. The deformation vector at non mesh points is obtained by using a cubic spline interpolation kernel. (32 or more parameters, 128 by default)

Required

Optimizer

The optimizer is the component that iteratively tweaks the transformation that matches the moving image with the fixed one. All optimizers have different characteristics and each have scenarios for which they are better suited than others.

Choosing one is usually a matter of trial and error and intuition. However optimizers do have their particular characteristics.

Regular Step Gradient Descent (Default): A Gradient Descent optimizer that reduces the step when it detects a change in the derivative, by multiplying the step by a relaxation factor. Fast and robust in early stages of registration, gets slower as the metric starts to converge. Good for early low resolution stages.

Conjugate Gradient: A Conjugate Gradient optimizer where each Gradient Descent iteration is followed by a line search to find the best value for the learning rate. Faster but less robust than Regular Step Gradient Descent. Better suited for a later stage of a multi stage registration process.

Quasi Newton: A Quasi Newton Optimizer with Broyden Fletcher Goldfarb Shannon (BFGS) minimization. This optimizer can be the metric to converge very quickly when the metric value is already small. It works well in the final stages of a multi stage registration process.

Amoeba: Implementation of the Nelder-Meade downhill simplex problem. It explores the parameter space by changing the shape of a convex polygon (specifically a simplex). When it converges, it can converge quickly from an early stage, but is very sensitive to local minima.

One Plus One Evolutionary: Use an evolutionary strategy to minimize the metric. This optimizer has a chaotic behavior that causes a large parameter space to be explored but it can be very slow at all stages. It should perform better with the Mattes Mutual Information metric.

Required

Depending on the value of the Optimizer parameter, different parameters will be available in the Termination Conditions and Optimization Parameters sections. These parameters are described below.

Regular Step Gradient Descent Parameters

Termination Conditions

Minimum Step

Halt when the Gradient Magnitude of the transformation parameters is less than this value.

Floating-point value. Default: 0.001.

Required

Tolerance

Halt when change in the metric is less than this value.

Floating-point value. Default: 0.001.

Required

Max Iterations

Halt after this number of iterations.

Positive integer. Default: 1500.

Required

Optimization Parameters

Estimate Learning Rate

Yes (Default): Automatically determine how quickly to alter the transformation in the direction of an improvement. More robust, but it may take much longer than setting a default.

No: Manually set a learning rate.

Required

Learning Rate

Learning coefficient in the gradient descent update equation.

Increase if the metric values are decreasing too slowly and decrease if the metric values are overshooting a potential minimum or producing results far from a reasonable registration.

Floating-point value. Default: 1.

Required when Estimate Learning Rate is set to No

Relaxation Factor

When the optimizer detects a change in the derivative of the metric, it multiplies the next step by a relaxation factor, to shorten it.

If the metric values are converging too slowly increasing this parameter towards 1 might help, while reducing it might aid convergence.

Floating-point value strictly between 0 and 1. Default: 0.5.

Required

Conjugate Gradient Parameters

Termination Conditions

Convergence Window

Look back at this many iterations to check for convergence.

Positive integer. Default: 10.

Required

Convergence Value Tolerance

Coefficient that determines the sensitivity of the convergence checker.

Floating-point value. Default: 1e-6.

Required

Max Iterations

Halt after this number of iterations.

Positive integer. Default: 1500.

Required

Optimization Parameters

Estimate Learning Rate	Yes (Default): Automatically determine a starting learning rate. No: Manually set a starting learning rate. Required
Initial Learning Rate	Initial value for the learning rate The learning rate is the learning coefficient in the gradient descent update equation. Increase if the metric values are decreasing too slowly and decrease if the metric values are overshooting a potential minimum or producing results far from a reasonable registration within a few iterations. Floating-point value. Default: 1. Required when Estimate Learning Rate is set to No
Minimum Learning Rate Multiplier	Control how much can the metric decrease between updates. The lower bound of the interval in which a line search is performed to find a learning rate is determined by multiplying the current learning rate value by this number. For example, if for one iteration the learning rate is 5, and the minimum learning rate multiplier is 0.5, the learning rate in the next iteration will be somewhere between 2.5 and the maximum. Non negative floating-point value. Default: 0. Required
Maximum Learning Rate Multiplier	Control how much can the metric increase between updates. The upper bound of the interval in which a line search is performed to find a learning rate is determined by multiplying the current learning rate value by this number. For example, if for one iteration the learning rate is 5, and the minimum learning rate multiplier is 1.5, the learning rate in the next iteration will be somewhere between the minimum and 7.5. When both the Maximum and the Minimum learning rate are set to 1, this optimizer behaves like a regular gradient descent optimizer. Positive floating-point value. Default: 20. Required
Line Search Tolerance	Control the accuracy of the line search. Positive floating-point value. Default: 0.2. Required

Quasi Newton Parameters

Termination Conditions

Convergence Window	Look back at this many iterations to check for convergence. Positive integer. Default: 10. Required
Convergence Value Tolerance	Coefficient that determines the sensitivity of the convergence checker. Floating-point value. Default: 1e-6. Required
Iterations Without Progress	Halt if the metric hasn’t improved in this many iterations. Positive integer. Default: 30. Required
Max Iterations	Halt after this number of iterations. Positive integer. Default: 1500. Required

Amoeba Parameters

Termination Conditions

Transformation Tolerance

Halt when the magnitude of the changes to the transformation parameters are less than this value.

Non negative floating-point value. Default: 1e-5.

Required

Metric Tolerance

Halt when the change in the metric is less than this value.

Non negative floating-point value. Default: 1e-5.

Required

Max Iterations

Halt after this number of iterations.

Positive integer. Default: 1500.

Required

Optimization Parameters

On Convergence

Terminate (Default): End the Optimization process once the termination conditions are met.

Restart: Restart the optimization process with a simplex with edges of half the size.

Required

One Plus One Evolutionary Parameters

Termination Conditions

Max Iterations

Halt after this number of iterations.

Positive integer. Default: 1500.

Required

Optimization Parameters

Initial Search Radius

Set the initial search radius.

The Search radius determines how different transformation parameter solutions can be in between steps.

Subsequent iterations will alter the search radius.

Increasing this parameter reduces computation time at the cost of accuracy.

Non negative floating-point value. Default: 0.1.

Required

Minimum Search Radius

Choose transformation parameter solutions further than this value.

Increasing will speed up convergence, but increases the risk of failing to converge.

Non negative floating-point value. Default: 1e-6.

Required

Advanced

Center Initialization	Center of Mass (Default): Compute the center of the images as the "center of mass" of the image, using pixel intensities, then align the centers of the images prior to registration. Extents: Use the image extents, described in the raster properties, to compute the center of the image, then align the centers of the images prior to registration. None: Register the images in the position they come in. Note: Even if the images are already aligned for registration, enabling the appropriate form of center initialization can still improve the performance and accuracy of this transformer. This happens because center initialization also alters the “center” of the image, which is used as a reference. Required
Metric Sampling Strategy	It is often not necessary to use all of the image to evaluate the metric, but a subset may suffice. This parameter controls the strategy used for selection of points for metric evaluation. Sampling happens on the already shrunken and smoothed image if those options are enabled. Regular (Default): Choose a subset of the pixels of the image, distributed according to a constant pattern, for metric evaluation. Random: Choose a random subset of points to evaluate the metric at. None: Use the whole image for metric evaluation. This is a slower option, useful when the image has a fine pattern. Required
Metric Sampling Rate	Control what percentage of pixels in the image to use for metric evaluation. Decreasing this parameter trades performance at the cost of accuracy. Values between 10 and 50 are generally good for medical images. Noisy or detailed images can register worse with lower sampling rates than other images. Floating-point value between 0 and 100. Default: 50. Required with a Metric Sampling Strategy other than None.
Shrink Factor	Shrink the images by this factor before registration. Greater values make registration happen on smaller images which can make registration faster and more robust in earlier stages. A shrink factor, combined with setting a non zero smoothing factor, makes the metric smoother. The input images are only shrunk for the registration process, the output raster will have the full resolution. This option is used to create a multi stage resolution process, by chaining multiple RasterRegisterers with decreasing shrink factors and smoothing factors. To register at full resolution set the shrink factor to 1. This option is used to create a multi stage resolution process, by chaining multiple RasterRegisterers with decreasing shrink factors and decreasing smoothing coefficients. Floating-point value greater than or equal to 1. Default: 1. Required
Smoothing Factor	Apply and control Gaussian Smoothing on the images. Positive values of this option set the standard deviation of the blur equation. 0 disables the Smoothing. A shrink factor, combined with setting a non zero smoothing factor, makes the metric smoother. This option is used to create a multi stage resolution process, by chaining multiple RasterRegisterers with decreasing shrink factors and decreasing smoothing coefficients. Non negative floating-point value. Default: 0. Required
Registered Extents From	Fixed Image (Default): The registered image will have the same extents as the fixed image. Pixel values will be interpolated. Good for the terminal stage of a multi stage registration as it allows for direct comparison of pixel values. Computed Transform: The registered image will have the extents obtained by applying the computed transformation. Doesn’t affect pixel values by only raster properties. Good for non terminal stages of a multi stage registration as it doesn’t require interpolation. Note: Neither option requires the images to have the same extents to begin with. Required when a rigid or affine transformation is selected
B-Spline Control Points per Dimension	Set the mesh size, by setting its number of control points per side. The mesh consists of a grid of vectors that represent a movement of a pixel from one point to another. A B-Spline transformation is constructed by manipulating these points. The BSpline transformation has 2*ControlPointsPerDimension^2 parameters, one parameter for each coordinate of each point of the mesh. This numbers grows quickly and optimizers can take longer to evaluate the step in the transformation. Integer greater than 4. Default: 8. Required when the Transformation is set to BSpline

Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.

How to Set Parameter Values

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Content Types

String Functions	These functions manipulate and format strings.
Special Characters	A set of control characters is available in the Text Editor.
Math Functions	Math functions are available in both editors.
Date/Time Functions	Date and time functions are available in the Text Editor.
Math Operators	These operators are available in the Arithmetic Editor.
FME Feature Functions	These return primarily feature-specific values.
FME Parameters	FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters	Create your own editable parameters.

Dialog Options - Tables

Transformers with table-style parameters have additional tools for populating and manipulating values.

Table Tools

Row Reordering

Enabled once you have clicked on a row item. Choices include:

Add a row
Remove a row
Move current row up one
Move current row down one
Move current row to top
Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

Cut a row - delete and copy to clipboard
Copy a row to the clipboard
Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

Reference

Processing Behavior	Feature-Based
Feature Holding	No
Dependencies	None
Aliases
History

FME Community

The FME Community is the place for demos, how-tos, articles, FAQs, and more. Get answers to your questions, learn from other users, and suggest, vote, and comment on new features.

Search for all results about the RasterRegisterer on the FME Community.

Examples may contain information licensed under the Open Government Licence – Vancouver and/or the Open Government Licence – Canada.

RasterRegisterer

Categories

Related Transformers

Typical Uses

How does it work?

The registration process

Selecting Raster Bands and Palette

Alpha Bands and Nodata values

Attributes

Coordinate system handling

Usage Notes

Choosing a Raster Transformer

Working with Rasters

Working with Bands

Working with Cells

Working with Palettes

Workflow Control

Vectors and Rasters

Configuration

Input Ports

Output Ports

Parameters

Editing Transformer Parameters

Defining Values

Using the Text Editor

Using the Arithmetic Editor

Conditional Values

Content

Dialog Options - Tables

Reference

FME Community