Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers

Yuliya Tarabalka (1, 2) , Jocelyn Chanussot (3) , Jon Atli Benediktsson (4)

1 GSFC - NASA Goddard Space Flight Center
2 GIPSA-lab - Grenoble Images Parole Signal Automatique
3 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
4 University of Iceland [Reykjavik]

A new method for segmentation and classification of hyperspectral images is proposed. The method is based on the construction of a minimum spanning forest (MSF) from region markers. Markers are defined automatically from classification results. For this purpose, pixelwise classification is performed, and the most reliable classified pixels are chosen as markers. Each classification-derived marker is associated with a class label. Each tree in the MSF grown from a marker forms a region in the segmentation map. By assigning a class of each marker to all the pixels within the region grown from this marker, a spectral-spatial classification map is obtained. Furthermore, the classification map is refined using the results of a pixelwise classification and a majority voting within the spatially connected regions. Experimental results are presented for three hyperspectral airborne images. The use of different dissimilarity measures for the construction of the MSF is investigated. The proposed scheme improves classification accuracies, when compared to previously proposed classification techniques, and provides accurate segmentation and classification maps.

Mots clés: segmentation; minimum spanning forest (MSF); marker selection; Classification; hyperspectral images

Domaines: Traitement des images [eess.IV]

Yuliya Tarabalka, Jocelyn Chanussot, Jon Atli Benediktsson. Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40 (5), pp.1267-1279. ⟨10.1109/TSMCB.2009.2037132⟩. ⟨hal-00578848⟩