# R Language Raster and Image Analysis

## Calculating GLCM Texture

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 `shift` parameter:

``````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)
`````` ## Mathematical Morphologies

The package `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. `disc`, `box` or `diamond`)

``````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 `erode()` function.

``````rArr <- as.array(r, transpose = TRUE)
rErode <- erode(rArr, sk)
rErode <- setValues(r, as.vector(aperm(rErode)))
``````

Besides `erode()`, also the morphological functions `dilate()`, `opening()` and `closing()` can be applied like this.

``````plot(rErode)
`````` 