Assessing the quality of heathland vegetation by classification of hyperspectral data using spatial information

This article deals with a method for acquiring vegetation maps, suitable for monitoring and evaluating the conservation status of heathland vegetation from hyperspectral data. The applied method is a recursive supervised segmentation algorithm based on a Tree-structured Markov Random Field (TS-MRF), capable of incorporating structural dependencies in the classification process. To this end, a tree structure is used that is built upon structural dependencies that are present in the field. The classification results from this TS-MRF with extended tree are compared to pixel-based classification results, results from a simple smoothing post-processing, and the result from the original binary TS-MRF technique.