I’m creating this topic because I was trying to reply a post from @AlfredWang (DJI Mavic 3M) but, unfortunately, it was closed.
I’m looking for multispectral pictures from DJI latest Mavic 3M but i couldn’t find any pack yet. If someone have similiar images and will to with me I would be so grateful.
If someone have a similiar pack of pictures or can communicate with him, I would be so grateful
If AlfredWang see this post, please leave an email where I can communicate with you
Sure, I can share my datasets. I’ll upload it to my google drive.
I have two datasets, they have different flight altitudes: 150 meters and 80 meters. The 150 meters one is about 3GB, another one is 14GB. So it’ll take some time to upload, when it finished, I’ll let you know and share the link.
Thanks for the MS images. I took a quick look at your datasets and found something that (probably) results in the error you described in DJI Mavic 3M
When I looked at your 150m dataset I saw that some images are missing. For the MS images at each GPS location 4 tiff’s are taken (…G.tiff , …NIR.tiff , …R.tiff and …RE.tiff)
When you sort the images by (filetype and name) you can see that some images are missing per group. The red brackets/groups are incomplete.
My guess, when you remove all the “red” images WebODM can proces them correctly.
The initial dataset was 186 images (with 4 bands) → 186 images / 4 bands = 46.5 “groups”. A round number is expected, this triggered my into looking if all the tiff images were stored.
The dataset with the incomplete image-groups were removed, 180 images / 4 bands = 45 groups. This is a round number and that should work (processing is currently running).
I guess Pix4D can “skip” an incomplete group of MS images where WebODM results in a processing error.
Funny is that I had the similar issue with the Phantom 4 MS, not all tiff images were stored or were 0kb. When I removed these incomplete groups it worked like a charm.
Thanks for your reply.
I checked the dataset and make sure groups for all bands are complete. Then I tried again and get some good results after upgrading my RAM to 40GB (from 16GB). So, I’m wondering the problem I met is caused by running out of memory. But interestingly, the error messages are not always like “lack of memory”, sometimes it’s just crashed.
Silly question, here, but just trying to work out multispectral. Is it just the images from the multispectral camera you upload from a single folder? ie do you leave out the RGB imagery?
Is the final result two orthoimages? The one is higher GSD with RGB bands, and another is lower GSD with MS bands?
If yes, that’s awesome and really simplify my working flow.
If not, how does it manage the data gap between RGB and MS images? As I know, the RGB camera usually has much higher resolution than MS cameras but lower pixel depth.
Thanks for posting the data! I couldn’t get the 150m dataset with the all images (RGB images + multispectral camera images) to run on my 16GB RAM computer. The only way I could complete the process is to use a subset of the data and to include only MS images from ~5 camera positions. Under plant health, I noticed that the NDVI algorithm value range changes when I play around with the filter. What role does the filter play in the output geoTIFF? For instance, why is there a difference in the NDVI between ‘RGN’ vs ‘GRReN’ even though the index only considers N and R? Since I only used the MS images with Green, Red, Red Edge, and Near Infrared cameras, should I just rely on the ‘GRReN’ filter for the output raster/image?
I apologize if these are rudimentary remote sensing/multispectral imaging questions, but I’m looking into buying the DJI Mavic 3M for environmental monitoring of stream riparian corridors so I’m hoping to keep this thread open and pick your brains about the drone model and WebODM. Thanks!
My understanding is that the filters direct the software as to which channels to use to run the various VI algorithms. So, for NDVI the formula is (NIR-red)/(NIR+red). The filter tells the software which bands to use in the formula, since different cameras will not always store the same data in the same channels.
Depending what you are doing, it is sometimes interesting to “cheat” the software by telling it fibs and saying you are using, for example, a camera which is set up to write channels NGB, whereas in reality your camera is RGN. This will force the software to use the “wrong” channels in the formula. Probably meaningless if you are using the software to accurately assess for plant health (as intended) but rather useful for looking for archaeological features!
