Supervised Classification

Performing a supervised landcover classification of multispectral satellites imagery for a study area. This project was conducted using both Maximum Likelihood and Support Vector Machine algorithms to compare results. Also making sure to generate around 200 testing points for accuracy assessment and using confusion matrix on outputs. The final highest accuracy result was made into a map layout.