I was supposed to map a 350 Ha hill that is covered with dense vegetation on the top. I’ve got 1664 pictures as a result of 5 flights with 85% frontal overlap and 80% side overlap. I flew my Phantom 4 Advanced 200m high from the homepoint and 120m from the top of the hill.
The thing is: there a a big part of missing data because the software wasn’t able to calibrate all the cameras (the software was able to calibrate only 87% of the cameras). That’s the second time that happened to me, the other time was in a similar project, mapping hills covered with dense vegetation on the top.
Does anybody knows how can I fix this problem? I really thougth that flying 120m high from the top of the hill with this amount of overlap would be enough, but apparently it isn’t.
It is tricky to get images calibrated in a dense vegetation. You should try half image scale (0.5) and Alternative Calibration method. You may be able to get more images calibrated. And also would you be able to upload the first page of your Quality Report, that includes the Median Number of Key Points, Number of Images Calibrated, Camera Optimization, Matching and GeoReferencing. I may be able to help you more if I can see those numbers.
I read in other post that I should try selecting the option “1/2 image scale” and inputting 40000 in the “Key point” field. I just started processing like that, hope it works…!
The manual interpretation of Median Number of Key Points varies for every projects but in your case since the default number 58692 going with a range of 35000 to 40000 would be a good option. By reducing the Median Number of Key Points you are going to be losing some detail however you would be increasing the changes of having more images calibrated. Images would have less key points but a stronger connection with each other. Moreover ideally you only want a single image block in your project. Changing the image scale to 0.5, calibration method to alternative and Median Number of Key Points from automatic to manual should help.
Hi Fabio, in my experience, I also sometimes found that case in my project and my solution is repeat fly the drone in higher altitude so less detail will be captured in the area. It is better for your drone to fly simultaneos following elevation. In hilly area you also should fly 200 above the ground so the GSD will be the same. I use sensefly drone and by inserting DSM data during the plan, the drone will fly following elevation.
I just got the results of the processing and unfortunately it still not good =(. It got better a little bit but there’s still lots of uncalibrated cameras and consequent errors as you can see in the images below:
I’ll try to process with 1/4 Image scale to see what happens. Any guess on how should I try next time?
Hey Fábio,
The disabled cameras are indicating that Pix4D didn’t even try to calibrate them whereas uncalibrated cameras indicating that Pix4D tried to calibrate them but was unsuccessful. Have you tried alternative calibration method yet? If you haven’t try that with 0.5 image scale and set Median Number of Key Points to Automatic, because 21000 seems kinda low for an RGB Project. Moreover try to add Manual Tie Points(MTP’s) if you haven’t yet. Also take a look at the link below: https://support.pix4d.com/hc/en-us/articles/202560159-How-to-improve-the-outputs-of-dense-vegetation-areas-#gsc.tab=0
Selim, I did that but it still didnt work… that’s the best result I’ve got:
To me those flying points looks like they’re good automatic tie points but ploted vertically instead of horizontally. Am I wrong?
There are around 200-300 uncalibrated cameras. How many MTP should I add per uncalibrated camera? And for how many uncalibrated cameras should I do that?
Is there a way to calibrate the cameras based mostly on geolocation than in recognition of similarities in the images? I may be talking crazy, but I can’t really understand why a data would be ploted correctly but in a weird/unexpected position rather than geolocation problems…
You can try Accurate Geolocation and Orientation calibration method, which would calibrate them based on their geolocation. However I would only use that method if you have a really accurate GPS like RTK.
hello kapil we are facing some problems after creating orthomasic at fully covered trees.orthomosaic is not properly aligned and stitched.
can you explain why this is happening
is there any changes that need
I’m afraid, you may not be able to use un-stiched images. With a dense vegetation, I found that the biggest factor is the wind. If you had any wind at all, causing the vegetation to move, the software will not be able to stitch them up. The software can only stitch steady pixels. If you have moving pixels, (bouncing tree tops) the software will not be able to compute them correctly. In case of dense vegetation area it is best to fly as low as possible, to pick the opening in between the trees, or get lucky and fly with the zero wind conditions.
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