land-cover classification (matlab) (maximum likelihood) Gaussian models [closed]

Remotely sensed data are provided as 6 images showing an urban area, with the ground-truth information. These images have already been registered. You are required to implement the Maximum Likelihood (ML) algorithm to classify the given data into four classes, 1 – building; 2 – vegetation; 3 – car; 4 – ground. By doing so, each pixel in the images will be assigned a class. There are four objectives to achieve the final classification goal.

To select training samples from given source data based on information in the given ground truth (at least 20 training samples for each class)

To establish Gaussian models for each class with the training samples;

To apply maximum likelihood to the testing data (measured data) and classify each pixel into a class;

To evaluate the classification accuracy by using a confusion matrix and visual aid (colour coded figures).