Supervised and unsupervised classification in Remote Sensing

It allows to execute supervised classification in satellite images through various algorithms.

mla(img, endm, model, training_split = 80, verbose = FALSE, ...)

Arguments

img	RasterStack or RasterBrick
endm	Signatures. Geometry type, Points or Polygons (typically shapefile), containing the training data.
model	Model to use. It can be Support Vector Machine (svm) like model = 'svm', Random Forest (randomForest) like model = 'randomForest', Naive Bayes (naiveBayes) like model = 'naiveBayes', Decision Tree (train) like model = 'LMT', Neural Networks (nnet) like model = 'nnet', K-nearest Neighbors (knn3) like model = 'knn'.
training_split	For splitting samples into two subsets, i.e. training data and for testing data
verbose	This parameter is Logical. It Prints progress messages during execution
...	Parameters to be passed to a machine learning algorithm. Please see svm, randomForest, naiveBayes, train, nnet and knn3

Note

If model = "LMT", the function is using "Logistic Model Trees" from http://topepo.github.io/caret/train-models-by-tag.html of the caret package.

References

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2013). An introduction to statistical learning : with applications in R. New York: Springer.

Mountrakis, G., Im, J., Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259.

Belgiu, M., Dragut., L. (2016). Random Forest in Remote Sensing: A Review of Applications and Future Directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.

Maxwell, A.E., Warner, T.A., Fang, F. (2018). Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 29(9), 2784-2817.

Pradhan, R., Ghose, M.K., Jeyaram, A. (2010). Land Cover Classification of Remotely Sensed Satellite Data using Bayesian and Hybrid classifier. International Journal of Computer Applications, 7(11).

Holloway, J., Mengersen, K. (2018). Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sensing, 10(9), 1365.

Examples

library(ForesToolboxRS)
library(raster)
library(snow)
library(caret)
library(sf)

# Load the datasets
data(img_l8)
data(endm)

# Random Forest Classifier
classMap <- mla(img = img_l8, endm = endm, model = "randomForest", training_split = 80)
print(classMap)

# Plot image
plot(classMap$Classification)