Output from 23121x41169 pixel image different from cropped area of same image


#1

I have a large 23121x41169 stitched survey shot with a Xiaom Yi and an IR-PRO filter. I uploaded the straight camera files for stitching instead of post-processed ones from ImageJ because the stitching fails with the ImageJ images.

My plan was to then take the final stitched survey and run it through ImageJ to get an NDVI result. (I understand the limitations of this approach and that it isn’t a true NDVI image I’m creating, but at least it lets the farmer see relative differences over time.) What happens when I process the entire image is that the results are just blue and green without any yellow or red components.

However, if I take one of the original, (component), images that went into the stitch and process that the same way, I get a result that has a range of blue, green, red, and yellow. I’ve done some further testing and extracted just a portion of the large stitched image, processed that, and still get the same blue and green result. Scaling it down to 10K pixels and 5K pixels width has the same result.

However, if I crop a more central portion that doesn’t have any black around the edges, (the stitched result is an odd shape, so areas outside of the survey are black), then I get a result that is closer to what I got from the one original component image.

I can’t quite wrap my brain around what is happening. The overall pixel dimensions don’t seem to be an issue as much as excluding the black border, but why would the black border cause an issue. I’d post examples, but the stitched image is quite large. You can see it here on MapsMadeEasy: https://www.mapsmadeeasy.com/maps/public/a33930a59a5447eeaaae5830c144b7f5

Thanks,

Mark


#2

I think the forum members need some more information about the way you processed and imported the images. Which Software did you use to stitch the images, which format do the (exported) images have.

How do you open the images in ImageJ since they are multi-spectral images (4 bands) and not an RGB.

And finally which OS do you use, how much RAM do you have available and which version of ImageJ, etc.

A posted example image (one tile with a black area) would be helpful, too.

Please note that there also plugins for ImageJ to handle hughe images or to import tiles of the image.
(Bioformats, ImgLib2, scifio). @ctrueden can say more about that.

Another strategy would be to extract the single planes with GDAL and then import them into ImageJ.


#3

Hi,

I used Maps Made Easy, an online survey stitching provider. The image is downloaded as a JPG.

In ImageJ, I just choose File, Open then process.

I am using Windows 10 64bit, 32GB of RAM and ImageJ version 1.50I.

I’ve attached four images:

  1. crop_no_black is a crop from the full size stitch that has none of the black border.
  2. crop_no_black_NDVI is the result of processing #1 in ImageJ.
  3. crop_with_black is a crop from the full size stitch that included black border.
  4. crop_with_black_NDVI is the result from processing #3 ImageJ

They all cover roughly the same area, but as you can see, very different results.

Thanks,

Mark

Ps. The image quality is pretty ugly since last year when I was taking these, the camera was not on a gimbal, and the vibration frequency did a nice job of turning things to Jello… :o) Flying with my DJI Inspire 1 Pro, I’m getting much better results!


#4

…and here are the images…


#5

It seems to be that the images (e.g., image#1) are already pseudo-colored in ‘Maps made easy’. This means the NDVI was calculated and a greyscale image was produced and pseudo-colored (a kind of heatmap) which you have then stored as an RGB (loosing the original calculated data form Maps made easy).

How does the original data look like? Which channels are in the original data and which channels (which bands) did you use for the calculation of the NDVI?

Can you download the calculated stitched NDVI data (image) from ‘Maps made easy’?

See here:

http://www.agisoft.com/forum/index.php?topic=2741.0

http://agribotix.com/blog/2014/6/10/misconceptions-about-uav-collected-ndvi-imagery-and-the-agribotix-experience-in-ground-truthing-these-images-for-agriculture