Needing to speed up a process involving calc() and cover() raster functions

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 want
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() :

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   foreach_1.4.2
     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       stringi_0.5-5
     stringr_1.0.0      tools_3.1.2

============================================================
Best,

Mathieu

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