Automatic fusion and classification of hyperspectral and LiDAR data using random forests

In this paper we discuss the use of the random forest algorithm for automatic fusion and classification of hyperspectral and LiDAR data. We demonstrate how relative feature relevance can be used in random forests to perform automatic and unsupervised feature selection. This allows using a large number of features without suffering from the curse of dimensionality. The effectiveness of the proposed approach is demonstrated on two datasets. The first dataset features a combination of hyperspectral and LiDAR data for urban classification whereas the second dataset is the well-known Indian Pines dataset featuring pure hyperspectral imagery. We show that by using the proposed approach classification accuracies can be improved significantly.