Classification of hyperspectral images by using morphological attribute filters 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 paper, a technique based on Independent Component Analysis (ICA) and morphological attribute filters is presented for the classification of high geometrical resolution hyperspectral images. The ICA is computed instead of the conventional principal component analysis (PCA) in order to better model the information in the hyperspectral image. The spatial characteristics of the objects in the scene are modeled by different multi-level attribute filters. Moreover, a method for increasing the robustness of the analysis based on a decision fusion strategy is proposed. A hyperspectral high resolution image acquired over the city of Pavia (Italy) was considered in the experiments.

Mots clés: Mathematical morphology; attribute filters; independent component analysis; decision fusion; remote sensing

Domaines: Traitement des images [eess.IV]

Mauro Dalla Mura, Alberto Villa, Jon Atli Benediktsson, Jocelyn Chanussot, Lorenzo Bruzzone. Classification of hyperspectral images by using morphological attribute filters and independent component analysis. WHISPERS 2010 - 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Jun 2010, Reykjavik, Iceland. conference proceedings. ⟨hal-00578909⟩