How about this:
b <- brick(system.file("external/rlogo.grd", package="raster"))
classified <- b / 255
threshs <- c(.1, .3, .5)
x <- classified < threshs
y <- max(x, na.rm=TRUE)
z <- reclassify(y, cbind(0, NA))
Robert
On Tue, Dec 22, 2015 at 1:57 AM, Mathieu Rajerison
<mathieu.rajerison at gmail.com> wrote:
Hi,
I use RSToolBox to classify a RGB raster.
I have a resulting RasterBrick which has as many layer as end members, in
my case 3 for different tones of blue.
I reclassify each band with calc to extract the pixels which have a small
angle mapping value. The threshold used is different depending on the
endmember layer.
I finally assembly all the bands with the cover function.
I needed to increase the memory limit assigned to R to have it worked. I
suspect that my code could be optimized, but I don't know in which way.
Here is the part of my code, that I think, could be optilmized, if you
to have a look and give some advice :
# RECLASSIFY
# classified is the classified RasterStack
# here I change the values of each band to 1 or NA depending on the
spectral angle mapping value.
# Is calc() slower than reclassify() for this purpose as I have only one
threshold value ?
threshs = c(.1,.2,.1)
for (i in 1:nlayers(classified )) {
clas = classified[[i]]
thresh=threshs[i]
out[[i]] = calc(clas, function(x) {x[x >= thresh] = NA;
x[x < thresh] = 1;
return(x)})
}
# COVERING
r = out[[1]]
for(i in 2:length(out)) {
r = cover(out[[i]], r) ## I cover by iteration
}
plot(r) # r is the final combined raster
================================================================
My sessionInfo() :
R version 3.1.2 (2014-10-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=French_France.1252 LC_CTYPE=French_France.1252
LC_MONETARY=French_France.1252
[4] LC_NUMERIC=C LC_TIME=French_France.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] R.utils_2.1.0 R.oo_1.19.0 R.methodsS3_1.7.0
igraph_1.0.1 scatterplot3d_0.3-36
[6] gdalUtils_2.0.1.7 spdep_0.5-88 Matrix_1.2-2
maptools_0.8-34 spgrass6_0.8-8
[11] XML_3.98-1.3 rgeos_0.3-8 FNN_1.1
rgdal_0.9-2 RStoolbox_0.1.1
[16] raster_2.3-40 sp_1.0-17
loaded via a namespace (and not attached):
[1] boot_1.3-13 car_2.0-25 caret_6.0-57 coda_0.18-1
codetools_0.2-9 colorspace_1.2-6
[7] deldir_0.1-9 digest_0.6.8 doParallel_1.0.8
foreign_0.8-61 geosphere_1.4-3
[13] ggplot2_1.0.1 grid_3.1.2 gtable_0.1.2
iterators_1.0.7 lattice_0.20-29 LearnBayes_2.15
[19] lme4_1.1-10 magrittr_1.5 MASS_7.3-35
MatrixModels_0.4-1 mgcv_1.8-3 minqa_1.2.4
[25] munsell_0.4.2 nlme_3.1-118 nloptr_1.0.4 nnet_7.3-8
parallel_3.1.2 pbkrtest_0.4-2
[31] plyr_1.8.3 proto_0.3-10 quantreg_5.19 Rcpp_0.12.0
reshape2_1.4.1 scales_0.2.5
[37] SparseM_1.7 splines_3.1.2 stats4_3.1.2
stringr_1.0.0 tools_3.1.2
============================================================
Best,
Mathieu
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