predict_rgb | R Documentation |
classify images using raster predict
predict_rgb( imageFiles = NULL, model = NULL, inPrefix = "index_", outPrefix = "classified_", bandNames = NULL, retRaster = TRUE )
imageFiles |
raster*. imagestack for classification purposes must contain the required bands as needed by the model. |
model |
model. classification model |
inPrefix |
character. in frefix string |
outPrefix |
character. out prefix string |
bandNames |
character. band names |
retRaster |
boolean if TRUE a raster stack is returned |
writes a result image in tif format if retRaster an rasterstack is returned
## Not run: ##- required packages require(uavRst) require(link2GI) ##- project folder projRootDir<-tempdir() ##-create subfolders pls notice the pathes are exported as global variables paths<-link2GI::initProj(projRootDir = projRootDir, projFolders = c("data/","data/ref/","output/","run/","las/"), global = TRUE, path_prefix = "path_") unlink(file.path(tempdir(),"*"), force = TRUE) ##- get the tutorial data utils::download.file("https://github.com/gisma/gismaData/raw/master/uavRst/data/ffs.zip", file.path(tempdir(),"ffs.zip")) unzip(zipfile = file.path(tempdir(),"ffs.zip"), exdir = tempdir()) ##- assign tutorial data imageFile <- file.path(tempdir(),"predict.tif") load(file.path(tempdir(),"tutorialbandNames.RData")) tutorialModel<-readRDS(file = file.path(tempdir(),"tutorialmodel.rds")) ##- start the prediction taking the non optimized model ##- please note the output is saved in the subdirectory path_output prediction<-predict_rgb(imageFiles=imageFile, model = tutorialModel[[1]], bandNames = bandNames) ##- visualise the classification raster::plot(prediction) ##+ ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.