Embedded feature selection of hyperspectral bands with boosted decision trees

Feature selection is an important step in hyperspectral analysis using machine learning for many applications, in particular to avoid the curse of dimensionality when there is limited available ground truth. This paper presents an approach to select hyperspectral bands using boosting. Boosting decision trees is an efficient and accurate classification technique that has been applied successfully to process hyperspectral data. The learned structure of the trees can provide insight about which bands are more relevant for the classification. We develop a method that takes into account the improvement obtained by each split of the tree ensemble and calculates a relative importance measure of the input features. The method was evaluated using hyperspectral data of rock samples from an iron ore mine in Australia. We show that by retaining only the most relevant features it is possible to reduce the computational load while retaining classification performance.