Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis

Mauro Dalla Mura (1, 2) , Alberto Villa (1, 3) , Jon Atli Benediktsson (1) , Jocelyn Chanussot (4) , Lorenzo Bruzzone (2)

1 University of Iceland [Reykjavik]
2 DISI - Department of Information Engineering and Computer Science
3 GIPSA-lab - Grenoble Images Parole Signal Automatique
4 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique

In this letter, a technique based on independent component analysis (ICA) and extended morphological attribute profiles (EAPs) is presented for the classification of hyperspectral images. The ICA maps the data into a subspace in which the components are as independent as possible. APs, which are extracted by using several attributes, are applied to each image associated with an extracted independent component, leading to a set of extended EAPs. Two approaches are presented for including the computed profiles in the analysis. The features extracted by the morphological processing are then classified with an SVM. The experiments carried out on two hyperspectral images proved the effectiveness of the proposed technique.

Mots clés: Attribute filters; remote sensing; independent component analysis (ICA); mathematical morphology; extended attribute profile (EAP); decision fusion

Domaines: Traitement des images [eess.IV]

Mauro Dalla Mura, Alberto Villa, Jon Atli Benediktsson, Jocelyn Chanussot, Lorenzo Bruzzone. Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geoscience and Remote Sensing Letters, 2011, 8 (3), pp.542-546. ⟨10.1109/LGRS.2010.2091253⟩. ⟨hal-00578886⟩