Supervised classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items and based on a training set of previously labeled items.
The supervised classification module is based on the Support Vector Machine method which consists in searching for the separating surface between 2 classes by the determination of the subset of training samples which best describes the boundary between the 2 classes. This method can be extended to be able to classify more than 2 classes.
The module allows to interactivelly describe learnings samples which corresponds to polygons samples on the input images.
Then a SVM model is derived from this learning sample which allows to classify each pixel of the input image in one of the defined class.
The non supervised classification module is based on the Kmeans algorithm. The GUI allows to modify parameters of the algorithm and produce a label image.