ABSTRACT
Decision tree (DT) is a nonparametric supervised learning method used for classification and regression. DT builds classification or regression models in the form of a tree. DTs can handle both categorical and numerical data. Processing algorithms on image, based on DT method, were used in the region of interest obtaining a classification error of 0.08% in the building stage. Tree-based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree-based methods are predictive models with high accuracy, stability and ease of interpretation. S. Valero Valbuena proposed the construction and processing of a new region-based, hierarchical, hyperspectral image representation known as the binary partition tree (BPT). The BPT succeeds in presenting the following: decomposition of the image in terms of coherent regions and inclusion of relations of the regions in the scene.
