R/rs_classify.R
calc_ext.RdConvenient function to preprocess synthetic raster bands from a given RGB image and/or DTM/DSM data.
Currently for all chanels of the input images the following textures and indices can be calculated:
rgb indices (17), color transformation (9), haralick (3 = 25 layers), statitics (4),edge (3),morpho (4), DEM Parameter (20).
All layers will be stacked (as an ENVI file).
If wanted the raster values can be extracted to a data frames by overlaying corresponding vector data. NOTE: The vector data has to be named identically to the rasterfiles. This is useful
for for classification training purposes and covers usually step 1 of the random forest based
classification of UAV derived visible imagery and point clouds.
calc_ext(
calculateBands = FALSE,
extractTrain = TRUE,
prefixRun = "temp",
patterndemFiles = "dem",
prefixTrainImg = "",
prefixTrainGeom = "",
suffixTrainImg = "",
suffixTrainGeom = "",
patternIdx = "index",
patternImgFiles = "",
channels = c("PCA"),
hara = TRUE,
haraType = c("simple", "advanced", "higher"),
stat = TRUE,
edge = TRUE,
edgeType = c("gradient", "sobel", "touzi"),
morpho = TRUE,
morphoType = c("dilate", "erode", "opening", "closing"),
rgbi = TRUE,
indices = c("VVI", "VARI", "NDTI", "RI", "SCI", "BI", "SI", "HI", "TGI", "GLI",
"NGRDI", "GRVI", "GLAI", "HUE", "CI", "SAT", "SHP"),
rgbTrans = TRUE,
colorSpaces = c("cielab", "CMY", "Gray", "HCL", "HSB", "HSI", "Log", "XYZ", "YUV"),
pardem = TRUE,
demType = c("hillshade", "slope", "aspect", "TRI", "TPI", "Roughness", "SLOPE",
"ASPECT", "C_GENE", "C_PROF", "C_PLAN", "C_TANG", "C_LONG", "C_CROS", "C_MINI",
"C_MAXI", "C_TOTA", "C_ROTO", "MTPI"),
saga_morphoMethod = 6,
minScale = 1,
maxScale = 8,
numScale = 2,
kernel = 3,
currentDataFolder = NULL,
currentIdxFolder = NULL,
cleanRunDir = TRUE,
sagaLinks = NULL,
gdalLinks = NULL,
otbLinks = NULL
)logical. switch for set on calculation of syntheic bands and indices default = TRUE
logical. switch for set on extract training data according to training geometries default = TRUE
character. general prefix of all result files of the current run default = "tmp"
character. mandantory if DEM data is processes. prefix of current DEM default = "dem"
character. potential string of characters that is used in the beginning of a shape file prefixTrainImg_SHAPEFILENAME_suffixTrainImg
character. potential string of characters that is used in the beginning of a shape file prefixTrainGeom_SHAPEFILENAME_suffixTrainGeom
character. potential string of characters that is used in the beginning of a shape file prefixTrainImg_SHAPEFILENAME_suffixTrainImg
character. potential string of characters that is used in the beginning of a shape file prefixTrainGeom_SHAPEFILENAME_suffixTrainGeom
character. code string that will concatenated to the filename to identify the index file
character. mandantory string that exist in ech imagefile to be processes
character. channels to be choosed options are c("PCA", red", "green", "blue") default = c("PCA") first principal component of the input image
logical. switch for using HaralickTextureExtraction, default = TRUE.
character. hara options, default is c("simple"), other options are "advanced" "higher" "all". NOTE: "higher" takes a LOT of time
logical. switch for using statistic default = TRUE the stas are mean,variance, curtosis, skewness
logical. switch for using edge filtering default = TRUE
character. edge options, default is c("gradient","sobel","touzi") all options are c("gradient","sobel","touzi")
logical. switch for using morphological filtering default = TRUE
character. morphological options, default is c("dilate","erode","opening","closing") all options are ("dilate","erode","opening","closing")
logical. switch for using rgbi index calcualtions default = TRUE
character. RGB indices, default is c("VARI","NDTI","RI","CI","BI","SI","HI","TGI","GLI","NGRDI") all options are c("VVI","VARI","NDTI","RI","SCI","BI","SI","HI","TGI","GLI","NGRDI","GRVI","GLAI","HUE","CI","SAT","SHP")
logical. switch for using color space transforming default = TRUE
character. RGB colorspace transforming to default c("cielab","CMY","Gray","HCL","HSB","HSI","Log","XYZ","YUV")
logical. switch for calculating dem parameter, default = FALSE
character. ("hillshade","slope", "aspect","TRI","TPI","Roughness")
numeric. saga morphometric method
numeric. in scale for multi scale TPI
numeric. max scale for multi scale TPI
numeric. number of scale for multi scale TPI
numeric. size of kernel for filtering and statistics, default is 3
NULL folder to image (and shape) data
NULL folder for saving the results
logical. TRUE logical switch for deleting the calculated tifs, default is TRUE
list. SAGA tools cli paths
list. GDAL tools cli paths
list. OTB tools cli paths
data frame containing for each drawn point the pixel values of the rasterstack data
(01) calc_ext() calculation of spectral indices, basic spatial statistics and textures and
extracting of training values over all channels according to training data
(02) ffs_train() training using random forest and the forward feature selection method
startTrain=TRUE
(03) calc_ext() with respect to the selected predictor variables you may calculate
the requested channels for all rgb data that you want to predict.
(04) prediction startPredict=TRUE
If the function is used for stand alone extraction of the training data please provide both, the imagestack containing the raster data plus the corresponding band names (usually saved as an Rdata file) and the corresponding shape file.
The workflow is intended to use the forward feature selection as decribed by Meyer et al. (2018).
This approach needs at least a pair of images that differ in time and/or space for a leave one location out validation mode. You may overcome this situation if you tile your image and provide for each tile seperate training data.
If you just want to classify a single image by a single training file use the normal procedure as provided by the trainControl function.
if (FALSE) {
require(uavRst)
require(link2GI)
# create and check the links to the GI software
sagaLinks<-link2GI::linkSAGA()
gdalLinks<-link2GI::linkGDAL()
otbLinks<-link2GI::linkOTB()
##- create and set folders
##- please mind that the pathes are exported as global variables
paths<-link2GI::initProj(projRootDir = tempdir(),
projFolders = c("data/","data/ref/","output/","run/","las/"),
global = TRUE,
path_prefix = "path_")
##- clean runtime folder
unlink(file.path(tempdir(),"*"), force = TRUE)
##- get the tutorial data
url<-"https://github.com/gisma/gismaData/raw/master/uavRst/data/tutorial_data.zip"
utils::download.file(url,
file.path(tempdir(),"tutorial_data.zip"))
unzip(zipfile = file.path(tempdir(),"tutorial_data.zip"),
exdir = R.utils::getAbsolutePath(path_run))
##- calculate some synthetic channels from the RGB image and the canopy height model
##- then extract the from the corresponding training geometries the data values aka trainingdata
trainDF <- calc_ext(calculateBands = TRUE,
extractTrain = TRUE,
suffixTrainGeom = "",
patternIdx = "index",
patternImgFiles = "rgb" ,
patterndemFiles = "chm",
prefixRun = "tutorial",
prefixTrainImg = "",
rgbi = TRUE,
indices = c("TGI","CI"),
channels = c("red"),
rgbTrans = TRUE,
hara = TRUE,
haraType = c("simple"),
stat = TRUE,
edge = TRUE,
morpho = TRUE,
pardem = TRUE,
#demType = c("slope", "MTPI"),
kernel = 3,
currentDataFolder = path_run,
currentIdxFolder = path_run,
sagaLinks = sagaLinks,
gdalLinks = gdalLinks,
otbLinks =otbLinks)
##- show the result
head(trainDF)
# use ffs_train as next step for rf classification issues
}