GrandLeez

Tutorial index

Download

• You can find this dataset at :http://logiciels.ign.fr/?Telechargement,20
  Datasets are available at the bottom of the page, in part test datasets. Then, UnZip the ".zip" archive.

Presentation

This dataset was created by L.Girod at the University of Oslo, Norway. This dataset was acquired to model a volcano model created by O.Galland. File present in the directory are :

• 200 images : R0040438.JPG ... R0040637.JPG taken with a RICOH GR DIGITAL 3 (800x600 px).
• Geolocation of images : GPS_WPK_Grand-Leez.csv
• File with images neighboor : FileImagesNeighbour.xml
• 2 commands scripts : UASGrandLeez.bat and UASGrandLeez.sh
• File with detailed commands : cmd_UAS_Grand-Leez.txt
• 2 file for coordinate system transform : SysCoRTL.xml and </>SysCoBL72_EPSG31370.xml</i>

During this tutorial, we will approach direct georeferencing concepts. We will apply the MicMac processing flow to process a forest Canopy Surface Model. For more details, go further in tutorials or directly in commands pages. This dataset is provided by "l’Unité Gestion des Ressources Forestières et des Milieux Naturels (GRFMN), Université de Liège". Contact: jo.lisein@ulg.ac.be

Tutorial

Conversion of image coordinates

OriConvert is used for 5 purposes:

1. Conversion of the embedded GPS data into the micmac format : OriTxtInFile
2. Generate the image pairs file
3. Change the coordinate system (from WGS84 to a locally tangent system) with the argument : ChSys=DegreeWGS84@SysCoRTL.xml
4. Compute relative speed of the camera (for GPS delay determination) : MTD1=1 CalcV=1
5. Select a sample of the image block (PATC) for camera calibration : NameCple=FileImagesNeighbour.xml ImC=R0040536.JPG NbImC=25

mm3d OriConvert OriTxtInFile GPS_WPK_Grand-Leez.csv Nav-Brut-RTL ChSys=DegreeWGS84@SysCoRTL.xml MTD1=1 NameCple=FileImagesNeighbour.xml CalcV=1 ImC=R0040536.JPG NbImC=25

See OriConvert for more details on arguments and file format.

Tie Point Generation with Tapioca

The file FileImagesNeighbour.xml contain for each images, his differents neighboors. If you open the file, you can see :

     <Cple>R0040439.JPG R0040519.JPG</Cple>
     <Cple>R0040439.JPG R0040514.JPG</Cple>
     <Cple>R0040439.JPG R0040444.JPG</Cple>
     <Cple>R0040439.JPG R0040517.JPG</Cple>
     <Cple>R0040439.JPG R0040438.JPG</Cple>
     <Cple>R0040439.JPG R0040440.JPG</Cple>
     <Cple>R0040439.JPG R0040441.JPG</Cple>
     <Cple>R0040439.JPG R0040516.JPG</Cple>
     <Cple>R0040439.JPG R0040442.JPG</Cple>
     <Cple>R0040439.JPG R0040515.JPG</Cple>
     <Cple>R0040439.JPG R0040443.JPG</Cple>

It means, image R0040439.JPG is connected with all the images detailed in <Cple> tag. So you can run the tie point generation with Tapioca using this file :

Tapioca File FileImagesNeighbour.xml -1

The processing time is shorter, because micmac knows which pictures matching.

Camera Calibration with Tapas, using a block of 25 images

To run a Camera calibration, you can take an other dataset, with exactly the same camera settings, or you can use a part of the principal dataset. Here we use the same images as in OriConvert to determine Internal Orientation Parameters (IOP) :

Tapas RadialBasic "R0040536.JPG|R0040537.JPG|R0040535.JPG|R0040578.JPG|R0040498.JPG|R0040499.JPG|R0040579.JPG|R0040538.JPG|R0040577.JPG|R0040534.JPG|R0040497.JPG|R0040500.JPG|R0040580.JPG|R0040456.JPG|R0040616.JPG|R0040576.JPG|R0040496.JPG|R0040617.JPG|R004045.JPG|R0040457.JPG|R0040615.JPG|R0040539.JPG|R0040501.JPG|R0040581.JPG|R0040533.JPG" Out=Sample4Calib-Rel

Orientation of the complete block in a relative system

You can directly integrate the IOP determination in the relative orientation processing, by using :

Tapas RadialBasic "R.*.JPG" Out=All-Rel Incal=Sample4Calib-Rel

We will know compute a sparse cloud with image relative position and orientation, to check if the block is correctly computed :

AperiCloud "R.*.JPG" All-Rel<pre>
Optionnaly, if meshlab is installed, you can vizualise the sparse cloud (Only for Ubuntu):
<pre>meshlab All-Rel.ply

1. Determination of the GPS delay (improvement of the georeferencing) with CenterBascule

CenterBascule "R.*.JPG" All-Rel Nav-Brut-RTL tmp CalcV=1

1. OriConvert is (again) used for taking the delay into account:

mm3d OriConvert OriTxtInFile GPS_WPK_Grand-Leez.csv Nav-adjusted-RTL ChSys=DegreeWGS84@SysCoRTL.xm MTD1=1 Delay=-0.0854304

1. Georefenrencing the aerotriangulated model with CenterBascule

CenterBascule "R.*.JPG" All-Rel Nav-adjusted-RTL All-RTL

1. Change the coordinate system with ChgSysCo:

mm3d ChgSysCo  "R.*JPG" All-RTL   SysCoRTL.xml@SysCoBL72_EPSG31370.xml   All-BL72

1. creation of the camera position (cloud):

AperiCloud "R.*.JPG" All-BL72 Out=All-BL72-cam.ply WithPoints=0

1. Optionnaly, if meshlab is installed:

meshlab All-BL72-cam.ply

1. Dense-matching with Malt. Here, we aren't interested in the generation of orthophoto.

Malt Ortho "R.*JPG" All-BL72 DirMEC=MEC DefCor=0 AffineLast=1 Regul=0.005 HrOr=0 LrOr=0 ZoomF=1

1. Convert the Digital surface model in 8bits:

to8Bits MEC/Z_Num8_DeZoom1_STD-MALT.tif

1. export the dense point cloud and color it with Nuage2Ply:

Nuage2Ply "MEC/NuageImProf_STD-MALT_Etape_8.xml" Scale=8 Attr="MEC/Z_Num8_DeZoom1_STD-MALT_8Bits.tif" Out=CanopySurfaceModel.ply

1. Optionnaly, if meshlab is installed:

meshlab CanopySurfaceModel.ply