Hi all -
I am trying to project a raster using the projectRaster() function from
the raster package and would like the data type for the output image to
be the same as the data type of the input image (INT2U Landsat 8 image
stack). For some reason I get an output data type of FLT8S, even when
04:35 PM, Ned Horning wrote:
Hi -
I had a problem a while back using sampleRandom with an extent object
to limit sampling within the extent and I thought it was fixed but now
there seems to be another problem. When I run sampleCells -
sampleRandom(predImage, size=20, sp=TRUE, ext=commonExt) I
Hi -
I had a problem a while back using sampleRandom with an extent object to
limit sampling within the extent and I thought it was fixed but now
there seems to be another problem. When I run sampleCells -
sampleRandom(predImage, size=20, sp=TRUE, ext=commonExt) I get the
following warnings
a datatype (FLT4S) argument
c - disaggregate(b, fact=32, filename='dis.tif', datatype='FLT4S')
Or if you set
rasterOptions(chunksize= ) temporarily to something lower.
I note that I should add a block_fun method to do this in one step
Robert
On Tue, Feb 18, 2014 at 9:47 AM, Ned Horning horn
Hi -
I have a script that calculates a common extent between two images than
then uses the resulting extent to restrict the random selection of cells
from a raster using the “sampleRandom” function. I use the following
function:
sampleCells - sampleRandom(predImage, size=numSamps,
- xyFromCell(r, s)
plot(e)
plot(r, add=T, legend=F)
points(xy)
On Mon, Nov 25, 2013 at 9:42 AM, Ned Horning horn...@amnh.org wrote:
Hi -
I have a script that calculates a common extent between two images than then
uses the resulting extent to restrict the random selection of cells from a
raster using
Thanks to everyone who responded to my question. This morning I tried
the approach suggested by Oscar Perpiñán Lamigueiro and was very
impressed. It is very fast, didn't consume excessive memory, and it
provides enough flexibility to use different statistics. It also
introduced me to the
Hi - Are there alternatives to using the raster package zonal function
for large images when using functions for the stat parameter? The
canned functions like 'mean' work well but I would like to write my own
functions to calculate standard deviation and other statistics.
I tried extract
Hi Mohammed,
I recently posted some random forests R scripts and guides on our web
site:http://biodiversityinformatics.amnh.org/. On the site go to Open
Source Resources = CBC Developed Resources. If you download and use
these tools I would appreciate your feedback. I still need to upload
this
Hi - I am working on a topographic correction script and am having a
problem with the gain and offset settings.
This is how I set the gain and offset:
gainVal - 0.108078
offsetVal - -0.37
gain(satImage) - gainVal
offs(satImage) - offsetVal
When I look at the gain/offset values they look fine:
Hi -
I am trying to extract values from image cells using the raster package
extract function:
classCellValues - extract(classImage, classCellNumbers)
The image that I am using has two do-data values that I set using
NAvalue. When I run extract I get lot of the following warnings:
In if
Hi - Before I embark to develop a script to create feature space plots
of two image bands I was wondering if anyone was aware of a package that
provides this capability. I would like to be able to plot pixel values
from one image band on the X-axis and pixel values from the another band
on the
Hi -
I am trying to select 1000 random samples from each set of ESRI
Shapefile polygons with the same attribute value (attName). This has
worked well in the past with other shapefiles. The file I'm using now
has 69 relatively simple polygons with 9 unique values for the attribute
I'm using
Hi -
I am getting the following error when I run aggregate (from the raster
package) on a raster image and output to a file.
Error in .GDALnodatavalue(dataformat) : cannot find matching nodata value
This is the command I am running:
aggregate(inImg, fact=40, fun=varPercent,
Hi Nick,
The Raster package has been my key to working with images in R.
Leveraging the Raster package you can use just about any classification
engine or statistics queries you want. I've been quite impress with the
RandomForest package (among others) for image classification. I'm happy
to
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