oneHotEncode R 中的光栅层

Question

I have a categorical raster layer that I need to one-hot encode, as I'm using neural networks to run a species distribution model (for a class) and it only works on continuous predictors. I've already run the model with the one-hot encoded data itself, but in order to predict onto a map, the raster itself needs to be one-Hot encoded in the same way.

In the RStoolbox package, oneHotEncode() should do what I need it to do, but I can't get it to work

nn.raster<-oneHotEncode(newraster$NLCD_2016_Land_Cover_oreg, classes=values, background = 0, foreground = 1, na.rm = FALSE)

Error message:

Error in.calcTest(x[1:5], fun, na.rm, forcefun, forceapply): cannot use this function. Perhaps add '...' or 'na.rm' to the function arguments?

Has anybody used this function and can help me troubleshoot? I think the problem is coming from the class's argument. My categories are numerical (from the national land cover raster), which is why they show up as "values" in the raster info. Do I need to do something to reclassify them? I think I'm naming them wrong but I'm not sure how.

Answer 1

It is difficult to help ass you are not providing a minimal, self-contained, reproducible, example . But, after giving a quick look at RStoolbox::oneHotEncode , I am under the impression that it does what raster::layerize also does.

library(raster)
r <- raster(nrow=20, ncol=20)
values(r) <- c(rep(NA, 50), rep(1:5, 70))
r
#class      : RasterLayer 
#dimensions : 20, 20, 400  (nrow, ncol, ncell)
#resolution : 18, 9  (x, y)
#extent     : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#crs        : +proj=longlat +datum=WGS84 +no_defs 
#source     : memory
#names      : layer 
#values     : 1, 5  (min, max)

b <- layerize(r)
b
#class      : RasterBrick 
#dimensions : 20, 20, 400, 5  (nrow, ncol, ncell, nlayers)
#resolution : 18, 9  (x, y)
#extent     : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#crs        : +proj=longlat +datum=WGS84 +no_defs 
#source     : memory
#names      : X1, X2, X3, X4, X5 
#min values :  0,  0,  0,  0,  0 
#max values :  1,  1,  1,  1,  1 

Which is equivalent to terra::separate

library(terra)    
r <- rast(nrow=5, ncol=5)
values(r) <- rep(c(1:4, NA), each=5)
b <- separate(r)