computing tie points

The basic command for computing tie points is Tapioca. If you don’t have very many images, you can use Tapioca to find the tie points by matching all pairs of images:

mm3d Tapioca MulScale "OIS.*tif" 400 1200

KH-9 Hexagon mapping camera

Because of the small number of images used in processing KH-9 Hexagon mapping camera images, the basic Tapioca call will usually be sufficient, and you can move on to the next step: finding the relative orientation.

Occasionally, it might be necessary to re-run this step at a higher resolution, to generate a more dense coverage of tie points - for example:

mm3d Tapioca MulScale "OIS.*tif" 800 8000

aerial images

the neighbours file

With a large number of images, this will be a very slow process. If you have vector data (e.g., a shapefile) of the image footprints, you can use spymicmac.micmac.write_neighbour_images() to narrow down the number of image pairs where Tapioca will search for pairs. This will create a file, FileImagesNeighbour.xml, that specifies which images overlap based on their footprints.

For more modern images where more precise location information is available, you can also use the OriConvert tool:

mm3d OriConvert OriTxtInFile GPS_sommets.txt Sommets NameCple=FileImagesNeighbour.xml

Then, you can run Tapioca using the File option:

mm3d Tapioca File FileImagesNeighbour.xml 1200

creating a mask

You can also create a mask to filter out tie points that are created due to the presence of fiducial marks or inscriptions on the images. The basic command for this is:

mm3d SaisieMasqQT "OIS-Reech_<Img>.tif"

A slightly more detailed instructional video can be found here. Once you have created the mask, be sure to rename the file:

mv OIS-Reech_<Img>_Masq.tif filtre.tif

filtering tie points

Once you have created a mask, you can use it to filter tie points:

mm3d HomolFilterMasq "OIS.*tif" GlobalMasq=filtre.tif

Once this is done, you can move on to computing the relative orientation using Tapas.