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'Best practice workflow' to process UAV survey datasets with highly accurate ground control points and Agisoft


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just found this interesting articles on Agisoft forum :



Here is what I think is the 'best practice workflow' to process UAV survey datasets with highly accurate ground control points (surveyed using cm accuracy RTK GPS). Note that this is my 'optimised' workflow for imagery acquired by DJI drones (Inspire 1, Phantom 4 Pro, Mavic Pro).

1. Import Photos

2. Manually remove images that are obvious 'outliers' (e.g., images that have been taken before take off etc)

3. Convert GPS coordinates of your geotagged images (WGS84) to match the coordinate system of your ground control points (GCPs) which will be imported later. Note that the altitude information stored in the EXIF data of imagery acquired by DJI drones is the relative altitude from the point of take off and not the absolute/real world altitude. Here are 3 ways to fix this issue: http://www.agisoft.com/forum/index.php?topic=4986.msg38769#msg38769

4. Estimate image quality. Disable all images that have an image quality below 0.7

5. Generate masks if necessary (for example, if you don't want to include cars or other moving objects). Optional.

6. Align photos (quality HIGH, pair preselection: REFERENCE, key point limit: 40,000, tie point limit: 4,000, adaptive camare model fitting: YES). Note that you do not need to run the image alignment process twice if you follow this workflow.

7. Import list of ground control points (also include the X/Y/Z accuracy values)

8. Verify and link markers to images (use FILTER BY MARKERS by right clicking on GCP). Because the acquired images and the markers now have the same coordinate reference system, it should be easy to find and mark your GCPs in your images. Mark each GCP in 3-6 images. That should be sufficient. When finished, press the UPDATE button in the reference pane.

9. Assuming that you have a sufficient number (~8 or more)  of high accuracy ground control points, uncheck all images in the reference pane and also uncheck a few GCPs (20 to 30%) in order to use them as check points instead of control points. This will give you a better measure of the 'real accuracy' of your dataset. Note that the layout/distribution of GCPs is very important (as pointed out by JMR). If you have been able to correct the EXIF altitude information for all your cameras (see point 3), then you do not have to uncheck the cameras in the reference pane. They can be used as reference as long as the right camera accuracy settings (leave default 10m) have been chosen.

10. Clean sparse point cloud (EDIT > GRADUAL SELECTION). Remove all points with high reprojection error (choose a value below 1, I suggest to use 0.5-0.8 ) and high reconstruction uncertainty (try to find the 'natural threshold' by moving the slider).

11. Adjust your bounding box

12. Optimize camera alignment (magic wand button)

13. Build dense cloud (I would normally choose HIGH or MEDIUM quality, but it depends on what you want to do with the data and on your hardware including CPU, GPU and RAM)

14. Build mesh (not needed if you just want a DEM and/or orthophotograph)

15. Build texture (not needed if you just want a DEM and/or orthophotograph)

16. Build DEM (from dense cloud)

17. Build Orthomosaic based on DEM

DONE  8)




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