A decision tree classifier design for high-dimensional data with limited training samples

Advances in sensor technology have increased the spectral resolution of remote sensing data significantly. Higher spectral resolution for each pixel should make possible the discrimination of a larger number of classes in more detail. However, due to the scarcity of training samples in remote sensing applications, the increase in spectral dimensionality only complicates the design of classifiers which, if not properly done, may cause the deterioration of classification accuracy. In this work, we propose a new design procedure for a hybrid decision tree classifier which improves the classification efficiency and accuracy for classifying high-dimensional data with a small training sample size. We further propose to use a feature extraction technique based on maximizing the statistical distance between two subgroups. Experimental results show that the proposed tree classifier is more effective in classifying high-dimensional data with limited training samples than a single-layer classifier and a previously proposed hybrid tree classifier