See also I/O for Raster Images
Gray Level Co-Occurrence Matrix (Haralick et al. 1973) texture is a powerful image feature for image analysis. The
glcm package provides a easy-to-use function to calculate such texutral features for
RasterLayer objects in R.
library(glcm) library(raster) r <- raster("C:/Program Files/R/R-3.2.3/doc/html/logo.jpg") plot(r)
Calculating GLCM textures in one direction
rglcm <- glcm(r, window = c(9,9), shift = c(1,1), statistics = c("mean", "variance", "homogeneity", "contrast", "dissimilarity", "entropy", "second_moment") ) plot(rglcm)
Calculation rotation-invariant texture features
The textural features can also be calculated in all 4 directions (0°, 45°, 90° and 135°) and then combined to one rotation-invariant texture. The key for this is the
rglcm1 <- glcm(r, window = c(9,9), shift=list(c(0,1), c(1,1), c(1,0), c(1,-1)), statistics = c("mean", "variance", "homogeneity", "contrast", "dissimilarity", "entropy", "second_moment") ) plot(rglcm1)
mmand provides functions for the calculation of Mathematical Morphologies for n-dimensional arrays. With a little workaround, these can also be calculated for raster images.
library(raster) library(mmand) r <- raster("C:/Program Files/R/R-3.2.3/doc/html/logo.jpg") plot(r)
At first, a kernel (moving window) has to be set with a size (e.g. 9x9) and a shape type (e.g.
sk <- shapeKernel(c(9,9), type="disc")
Afterwards, the raster layer has to be converted into an array wich is used as input for the
rArr <- as.array(r, transpose = TRUE) rErode <- erode(rArr, sk) rErode <- setValues(r, as.vector(aperm(rErode)))
erode(), also the morphological functions
closing() can be applied like this.