Feature Selection by Using Classification and Regression Trees (cart)

Hyper-spectral remote sensing increases the volume of information available for research and practice, but brings with it the need for efficient statistical methods in sample spaces of many dimensions. Due to the complexity of problems in high dimensionality, several methods for dimension reduction are suggested in the literature, such as Principal Components Analysis (PCA). Although PCA can be applied to data reduction, its use for classifying images has not produced good results. In the present study, the Classification and Regression Trees technique, more widely known by the acronym CART, is used for feature selection. CART involves the identification and construction of a binary decision tree using a sample of training data for which the correct classification is known. Binary decision trees consist of repeated divisions of a feature space into two sub-spaces, with the terminal nodes associated with the classes. A desirable decision tree is one having a relatively small number of branches, a relatively small number of intermediate nodes from which these branches diverge, and high predictive power, in which entities are correctly classified at the terminal nodes. In the present study, AVIRIS digital images from agricultural fields in the USA are used. The images were automatically classified by a binary decision tree. Based on the results from the digital classification, a table showing highly discriminatory spectral bands for each kind of agricultural field was generated. Moreover, the spectral signatures of the cultures are discussed. The results show that the decision trees employ a strategy in which a complex problem is divided into simpler sub-problems, with the advantage that it becomes possible to follow the classification process through each node of the decision tree. It is emphasized that it is the computer algorithm itself which selects the bands with maximum discriminatory power, thus providing useful information to the researcher.