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The automatic classification of land cover types from hyperspectral images is a challenging problem due to (among others) the largeamount of spectral bands and their high spatial and spectral correlation. The extraction of meaningful features, that enables a subsequentclassifier to distinguish between different land cover classes, is often limited to a subset of all available data dimensions which is foundby band selection techniques or other methods of dimensionality reduction. This work applies Projection-Based Random Forests tohyperspectral images, which not only overcome the need of an explicit feature extraction, but also provide mechanisms to automaticallyselect spectral bands that contain original (i.e. non-redundant) as well as highly meaningful information for the given classificationtask. The proposed method is applied to four challenging hyperspectral datasets and it is shown that the effective number of spectralbands can be considerably limited without loosing too much of classification performance, e.g. a loss of 1 % accuracy if roughly 13 %of all available bands are used

TASK-DEPENDENT BAND-SELECTION OF HYPERSPECTRAL IMAGES BY PROJECTION-BASED RANDOM FORESTS