Kernel Principal Component Analysis for the construction of the extended morphological profile

Mathieu Fauvel (1) , Jocelyn Chanussot (2) , Jon Atli Benediktsson (3)

1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
3 University of Iceland [Reykjavik]

Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remote-sensing data. Features extracted using KPCA are used to construct the Extended Morphological Profile (EMP). Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principal component analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data. The overall classification accuracy increases from 79% to 96% with the proposed approach.

Domaines: Traitement des images [eess.IV]

Mathieu Fauvel, Jocelyn Chanussot, Jon Atli Benediktsson. Kernel Principal Component Analysis for the construction of the extended morphological profile. IGARSS 2009 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2009, Le Cap, South Africa. pp.II:843-846, ⟨10.1109/IGARSS.2009.5418227⟩. ⟨hal-00449456⟩