updated README

README.md

@@ -5,9 +5,9 @@
 [![codecov](https://codecov.io/gh/bleutner/RStoolbox/branch/master/graph/badge.svg)](https://app.codecov.io/gh/bleutner/RStoolbox)
 [![Downloads](http://cranlogs.r-pkg.org/badges/RStoolbox)](https://www.r-pkg.org:443/pkg/RStoolbox)
 
-RStoolbox is an R package providing a wide range of tools for your every-day remote sensing processing needs. The available tool-set covers many aspects from data import, pre-processing, data analysis, image classification and graphical display. RStoolbox builds upon the terra package, which makes it suitable for processing large data-sets even on smaller workstations.
+`RStoolbox` is an R package providing a wide range of tools for your every-day remote sensing processing needs. The available tool-set covers many aspects from data import, pre-processing, data analysis, image classification and graphical display. `RStoolbox` builds upon the `terra` package, which makes it suitable for processing large data-sets even on smaller workstations.
 
-For more details have a look at the [functions overview](https://bleutner.github.io/RStoolbox/reference/index.html).
+Find out more on the [`RStoolbox` webpage](https://bleutner.github.io/RStoolbox/).
 
 ## Installation
 The package is available on CRAN and can be installed as usual via
@@ -60,12 +60,13 @@ train <- readRDS(system.file("external/trainingPolygons_lsat.rds", package="RSto
 
 # plot input data
 ggRGB(lsat, r = 3, g = 2, b=1, stretch = "lin") +
-  geom_sf(data = train, aes(fill = class))
+  geom_sf(data = train, aes(fill = class)) + 
+  scale_fill_manual(values = c("yellow", "sandybrown", "darkgreen", "blue"))
 #> Coordinate system already present. Adding new coordinate system, which will
 #> replace the existing one.
 ```
 
-![](https://i.imgur.com/yhLQh7O.png)<!-- -->
+![](https://i.imgur.com/s071ieD.png)<!-- -->
 
 ``` r
 
@@ -105,7 +106,7 @@ ggR(r, geom_raster = T, forceCat = T) + scale_fill_manual(values = c("yellow", "
 
 ### Example 2: Spectral Unmixing
 
-`RStoolbox` offers spectral unmixing by implementing the Multiple Endmember Spectral Mixture Analysis (MESMA) approach for estimating fractions of spectral classes, such as spectra of surfaces or materials, on a sub-pixel scale. The following workflow shows a simple Spectral Mixture Analysis (SMA) with single endmembers per class:
+`RStoolbox` offers spectral unmixing by implementing the Multiple Endmember Spectral Mixture Analysis (MESMA) approach for estimating fractions of spectral classes, such as spectra of surfaces or materials, on a sub-pixel scale. The following workflow shows a simple Spectral Mixture Analysis (SMA) with single endmembers per class, extracted from the `lsat` example image by cell id:
 
 ``` r
 library(RStoolbox)