Contents

Registering an Aerial Photo to an Orthophoto

This example shows how to register an aerial photo to an orthophoto. Two images of the same scene can only be compared directly if they are in the same coordinate system. Image registration is the process of transforming one image into the coordinate system of another image.

Step 1: Read Images

The image westconcordorthophoto.png is an orthophoto that has already been registered to the ground. The image westconcordaerial.png is unregistered as it was taken from an airplane and is distorted relative to the orthophoto.

unregistered = imread('westconcordaerial.png');
figure, imshow(unregistered)
text(size(unregistered,2),size(unregistered,1)+15, ...
    'Image courtesy of mPower3/Emerge', ...
    'FontSize',7,'HorizontalAlignment','right');

ortho = imread('westconcordorthophoto.png');
figure, imshow(ortho)
text(size(ortho,2),size(ortho,1)+15, ...
    'Image courtesy of Massachusetts Executive Office of Environmental Affairs', ...
    'FontSize',7,'HorizontalAlignment','right');

Step 2: Load and Add Control Points

Four pairs of control points have already been picked. Load these points from a MAT-file. If you want to proceed with these points, go to Step 3: Infer Geometric Transformation.

load westconcordpoints

Optionally, edit or add to the pre-picked points using the Control Point Selection Tool (cpselect). cpselect helps you pick pairs of corresponding control points. Control points are landmarks that you can find in both images, like a road intersection, or a natural feature. The unregistered image is an RGB image but cpselect only takes grayscale images, so you will pass it one plane of the RGB image.

cpselect(unregistered(:,:,1),'westconcordorthophoto.png',...
         movingPoints,fixedPoints)

Save control points by choosing the File menu, then the Save Points to Workspace option. Save the points, overwriting variables movingPoints and fixedPoints.

Step 3: Infer Geometric Transformation

Because we know that the unregistered image was taken from an airplane, and the topography is relatively flat, it is likely that most of the distortion is projective. fitgeotrans will find the parameters of the projective distortion that best fits the stray movingPoints and fixedPoints you picked.

t_concord = fitgeotrans(movingPoints,fixedPoints,'projective');

Step 4: Transform Unregistered Image

Even though the points were picked on one plane of the unregistered image, you can transform the entire RGB image. imwarp will apply the same transformation to each plane. Note that the specification of the 'OutputView' ensures the registered image will be aligned for elementwise comparison with the orthophoto.

Rfixed = imref2d(size(ortho));
registered = imwarp(unregistered,t_concord,'OutputView',Rfixed);

Step 5: View Registered Image in Context of Orthophoto

figure, imshowpair(ortho,registered,'blend')

Compare visually how well the registered image overlays on the orthophoto. Try going back to Step 2: Choose Control Points and using more than four pairs of points. Are the results better? What if the points are clumped together?

If you want to experiment with larger images, follow the steps above to register concordaerial.png to concordorthophoto.png.

Was this topic helpful?