Use the Automatic Image Registration Workflow to geometrically align two images with different viewing geometry and/or different terrain distortions into the same coordinate system.

See the following for help on a particular step of the workflow:

Workflow Tips

This workflow is not “modal,” meaning it will not block you from using other ENVI tools or working with additional data. This is useful in that the workflow will not prevent you from doing multiple things at a time. However, be aware that if you close all of your files in the middle of the workflow process, you might not be able to continue the workflow and will need to start over.

Navigating Workflow Steps

The number of steps provided in the workflow will depend on the input image data. For example, not all images will contain the data needed for every step; therefore, some steps will be skipped automatically.

Some steps can be optional; in those cases, the Perform this step radio button is selected by default. To skip that step and go to the next step in the workflow, select the Skip this step radio button, then click Next.

The timeline at the bottom of the workflow will display the order of steps available for the workflow and your data, and the title of your current location in the workflow will flash. The title is also an active link that you can click, to jump backward or forward to a desired step in the workflow.

Preview/Display Result

Some workflow steps provide options to preview the settings and/or to display the processed result.

  • Enable the Preview check box to see a preview of the settings before you click OK and process the data. The preview is calculated only on the area in the view and uses the resolution level at which you are viewing the image. See Preview for details on the results. To preview a different area in your image, pan and zoom to the area of interest and re-enable the Preview option.
  • Enable the Display result check box to display the raster in the view when processing is complete.

Open Workflow in Modeler

On the last step of the workflow, the Open Workflow in Modeler link will take your full workflow - the exact data, choices, and parameter values that you selected - and create a Model that can be manipulated in the ENVI Modeler. For example, you could create a Model to perform batch processing with multiple similar input datasets.

Select Data

You will need two images for image registration; one image is base, one image is warp.

  1. From the Toolbox, select Workflows > Automatic Image Registration Workflow. The Select Data panel appears.
  2. Select a Base Raster, and click OK.
  3. Select a Warp Raster, and click OK. This is the raster that will be registered or warped to match the geometry of the base raster.
  4. Click Next.

Generate

The Generating Tiepoints panel has settings that determine how to generate the tie points.

  1. Select the tie points Method to use from the drop-down list:

    • Generate Tie Points by Mutual Information (default): Performs automatic tie point generation using mutual information as a similarity measure. This method is optimized for registering images with different modalities (e.g., registering SAR with optical images, or thermal with visible images).

    • Generate Tie Points by Cross Correlation: Performs automatic tie point generation using cross correlation as a similarity measure. This method works well for general purposes, especially registering images with similar modality (e.g., registering optical images with optical images).

  2. Optionally choose a .pts file that contains Input Seed Tie Points.

  3. Enter the Requested Number of Tie Points to use for image registration. The minimum is 9; however, the recommended value is the default number that appears, which is based on the input file. Though smaller values provide quicker processing time, they do not result in an alignment as precise as one that used more tie points.
  4. Set the tolerance for the search range in the Search Window Size field. This moving window is a subset of the warp image that is searched to find topographic feature matches for tie point placement. The recommended value is the default number that appears, which is based on the input file.
  5. Set the Matching Window Size used for computing the matching score between the two images. The default value is 61.

  6. Specify the Interest Operator to use to identify feature points. The default value is Forstner.

    • Forstner: (default): Obtains and analyzes the gray scale gradient matrix between one pixel and its adjacent pixels. The Forstner operator is typically better for image matching than the Moravec operator.
    • Harris: Improves upon Moravec by using the auto-correlation matrix and avoids using discrete directions and shifts.
    • Moravec: Searches for gray scale value differences between one pixel and its adjacent pixels. The Moravec operator is typically faster than the Forstner operator.

Filter

In the Filtering Tiepoints panel, set the parameters to use in the filtering step.

  1. In the Method drop-down list, select one of the following:

    • Filter Tie Points by Fundamental Matrix: Uses the fundamental matrix to constrain the location of the tie points.

    • Filter Tie Points by Global Transform: Uses the global transform to filter tie points. For orthorectified images, nadir, or near-nadir images, the transformation model between the first and second image fits an RST transform. When the scene is rather flat and the sensor is very far from the scene, the transformation model between the two images fits a first-order polynomial transform. Global transform is the most common filtering method.

  2. Select the Fitting Transform to use when automatically filtering tie points.

    • First-Order Polynomial (default): A first-order polynomial warp includes an XY interaction term to account for image shear.
    • RST: Rotation, scaling, and translation; this is the simplest method. It does not allow for shearing in the image warp.
  3. If using the Filter Tie Point by Global Transform method, enter the maximum error to allow for each tie point in the Maximum Allowable Error Per Tie Point. The tie point with the largest error distance from the predicted location is iteratively removed until no tie points have an error greater than the this value. The default value is 5.00 pixels. Setting this field to a higher value means less accuracy.

Register

  1. Select a Warping Method from the drop-down list. The choices are as follows:

    • RST: Performs a full projection transformation (including datum shift, if needed) for every pixel in the output image.

    • Polynomial (default): A first-order polynomial warp includes an XY interaction term to account for image shear:

      x = a1 + a2X + a3Y + a4XY

      y = b1 + b2X + b3Y + b4XY

    • Triangulation: Delaunay triangulation warping fits triangles to the irregularly gridded data points and interpolates values to the output grid. This is the default option.

  2. Specify the optional Polynomial Degree when using the Polynomial warping method. The default value is 1.

  3. Select one of the following resampling options from the Resampling Method drop-down list:

    • Nearest Neighbor: Uses the nearest pixel without any interpolation to create the warped image.
    • Bilinear (default): Performs a linear interpolation using four pixels to resample the warped image.
    • Cubic Convolution: Uses 16 pixels to approximate the sinc function using cubic polynomials to resample the image. Cubic convolution resampling is significantly slower than the other methods.

  4. Set the optional Data Ignore Value, which is the pixel value used to fill areas where no image data appears in the warped image.

  5. Select Yes or No to specify whether to Output Full Extent. If No, the result will be the overlapping area only.

  6. Set the optional Output Pixel Size in the map unit of the base raster. The default output pixel sizes are the same as those of the base raster.

Export

The Export Final Result panel appears.

  1. Enter a filename and location for the Output Raster.
  2. Click Finish.

See Also

FilterTiePointsByGlobalTransform Task, FilterTiePointsByFundamentalMatrix Task, GenerateTiePointsByCrossCorrelation Task, GenerateTiePointsByMutualInformation Task