Hi,
I don't see anything in OTB that does exactly what you need.
The approach you propose seems the right one. I see two other ways :
Build a new filter using the Sampling framework. You may
re-implement the ThreadedGenerateData() to use your thread pool.
ImageSampleExtractorFilter is a good starting point.
Using existing OTB filters. Iterate over polygons, for each
one :
copy it alone in a in-memory OGRDataSource
extract an ROI of the image over the polygon extent
rasterize the polygon in a binary mask over the input image
ROI
with ManageNoData, apply the binary mask as a no-data on the
input image ROI
use ComputeImagesStatistics with the background value
corresponding to no-data
store the results in the current polygon.
My 2 cents,
Guillaume
On 08/03/2018 05:34 PM, Jordi Inglada
wrote:
Hi,
Given an image (raster) and a vector file containing polygons, I need to
compute a series of values for each polygon like the mean of a
particular band of the raster, etc. and then produce a new (or update
the) vector file with new fields. I think these are called zonal
statistics.
It looks like the LSMSVectorization application does that, but it uses a
raster segmentation instead of an input vector file. Given that my
polygons cover only a small fraction of the input raster, I don't think
than rasterizing the vector file to use this application will be
efficient. An besides, I would have to link back the attributes to the
original polygons, which is an additional step.
Is there any other way to do that with existing OTB applications? Maybe
gdal command line tools can do that? I have been looking around and it
seems that this is usually done the way LSMSVectorization does it (burn
the vector to raster, then compute using rasters only).
Do you have any advice on how to proceed if I need to write my own
application for that? I guess I will have to if I want to be able to
compute other features than mean and variance of the raster bands.
By the way, my images and vector data are huge, so streaming and
multi-threading will be needed.
I was thinking about the following approach:
1. Read in a set of polygons until a total surface is reached (so that
we can associate that with the RAM needed for the total number of raster
pixels to keep in memory).
2. Project the polygons onto the raster geometry and read in the pixels
3. Parallel computation of the stats (either with an OpenMP loop or a
simple thread pool [1])
4. Write the geometries and the stats to the output file
5. Release the memory and go back to 1
Any comments on that?
Thanks.
Jordi
[1] https://jordiinglada.net/wp/2015/05/13/a-simple-thread-pool-to-run-parallel-prosail-simulations/
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Département Payload Data & Applications
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