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
When working with KH-9 images, the single Tapioca
call will usually be sufficient, and you can move on to the
next step: finding the relative orientation.
air photos
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
.