I’m curious about the use of ABGPS (Airborne GPS) insofar as your Step 1. processing options were chosen. Please confirm what is known and correct my assumptions as need be:
- The DJI Inspire 1 is not survey grade; it geotags images with 2D GNSS fixes; i.e., only x and y; and uses its barometric data for geotagging the z. I’ve seen camera positions to be hundreds of feet off from reality using the Inspire 1.
- Step 1., Processing Options in Pix4Dmapper are set with the all of default settings for the mapping project in question. (?)
- The 9’ bias in z that you’re reporting is seen in all GCPs. (?)
- All of the ground control points have been surveyed with precise survey grade precision; e.g., Dual Freq. GNSS RTK. (?)
If you could, please post a screen snippet from the QC Report showing Geolocation Details for the project in question.
Also, please post a snippet of the control layout/ distribution. GCP positions will influence the processing.
Random thoughts and in addition to what Steve has mentioned…
Image quality matters. The lack of contrast of the (3) mostly white images in your image above may exacerbate issues while processing. While I haven’t deliberately used overexposed images in my tests over the past 2 months, I did have a set of images which were in general, underexposed (Manual Exposure, Canon 40D) toward the conclusion of collecting nearly 300 images, shot an hour before sunset when Exposure Values change quickly. I also have had sets of images which were shot under bright late morning conditions using the camera’s Automatic Exposure setting instead of Manual Exposure (Canon 40D). In both of these situations, and using Adobe Camera Raw, the entire set of images were Auto Leveled (and Chromatic Aberration) removed and saved. Lightroom can also batch process levels. If you have ACR or LR, you might try processing again after the images are auto leveled to see what, if any impacts there are in the post-processed results. Just be sure not to change any aspects of image size or lens properties.
It’s good that you did a second set of RTK observations on a different day, but you may well know, GNSS RTK can produce bad fixes, poor RMS values and unrepeatable positions depending upon a wide variety of issues, most notably due to multipath. If your RTK gear is able to generate a statistical report, and you can see one outlier with dubiously high RMS values (particularly on both days), try changing its Type from GCP to Manual Tie Point (MTP) and see what happens. In this test above, I was looking at the validity of points on the outer edges of the project - more on that later.
If your GCPs are from real survey data; i.e., tied to a known Spatial Reference System, the Vertical height system; e.g., NAVD88, GEOID12B, already has taken worries about geoidal separation into consideration so I’m not sure what you meant by the “need to double check Vertical datum heights vs. Pix4d.”
You mentioned processing Step 1. after importing your images and before entering the GCPs. Note that this is perfectly fine, but unnecessary as GCP entry can be done as soon as you’ve started your project and have imported your images. You will need to use the Basic GCP/MTP Editor instead of the rayCloud sidebar properties editor.
It’s also worth mentioning the Pix4Dmapper has a marvelous feature once you have found and marked your GCPs and MTPs in a project. In addition to Importing (and Exporting) your GCPs, the GCP/MTP Manager allows you to Export the marks; and then, in a new project Import them all saving lots of time. This is very handy when you’re using the same photos in different Pix4D projects.
Good luck and please post how you’re making out.