Title :
Spatial Classification of Hyperspectral Data of Dune Vegetation along the Belgian Coast
Author :
Thoonen, G. ; De Backer, S. ; Provoost, S. ; Kempeneers, P. ; Scheunders, P.
Author_Institution :
Vision Lab., Univ. of Antwerp, Antwerp
Abstract :
This work evaluates a classification method, including spatial information, for dune vegetation along the Belgian coastline. The used method is a recursive supervised segmentation algorithm based on a tree-structured Markov Random Field. This technique describes a K-ary field as a sequence of binary Markov Random Fields, each of which is represented by a node in the tree. The obtained classification results were compared to results with the same data set, for a purely spectral classification and a spectral classification, followed by spatial smoothing.
Keywords :
Markov processes; airborne radar; image classification; image segmentation; trees (mathematics); vegetation mapping; Belgian Coast; Europe; TS-MRF model; airborne data; binary Markov Random Field; dune environment; dune vegetation mapping; hyperspectral data; spatial classification; spatial smoothing; supervised segmentation algorithm; tree-structured Markov Random Field; Bayesian methods; Binary trees; Hyperspectral imaging; Hyperspectral sensors; Image segmentation; Markov random fields; Parameter estimation; Smoothing methods; Spatial resolution; Vegetation mapping; Image classification; Maximum likelihood estimation; Stochastic fields; Vegetation mapping;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
DOI :
10.1109/IGARSS.2008.4779389